RESOLVING LEXICAL AMBIGUITY IN A 
DETERMINISTIC PARSER 
Robert Milne 
Intelligent Applications 
10 Charlotte Square 
Edinburgh EH2 4DR Scotland 
Lexical ambiguity and especially part-of-speech ambiguity is the source of much non-determinism in 
parsing. As a result, the resolution of lexical ambiguity presents deterministic parsing with a major test. 
If deterministic parsing is to be viable, it must be shown that lexical ambiguity can be resolved easily 
deterministically. In this paper, it is shown that Marcus's "diagnostics" can be handled without any 
mechanisms beyond what is required to parse grammatical sentences and reject ungrammatical 
sentences. It is also shown that many other classes of ambiguity can be easily resolved as well. 
1 INTRODUCTION 
Lexical ambiguity, and especially part-of-speech ambigu- 
ity, is the source of much non-determinism in parsing. 
As a result, the resolution of lexical ambiguity presents 
deterministic parsing (Marcus 1980) with a major test. If 
deterministic parsing is to be viable, it should be shown 
that lexical ambiguity can be resolved deterministically 
for many situations in which people do not have trouble. 
In this paper, it is shown that Marcus's "diagnostics" can 
be handled without any mechanisms beyond what is 
required to parse grammatical sentences and reject 
ungrammatical sentences and that many other classes of 
ambiguity can be easily resolved as well. This result is 
possible because of the constraints on English from word 
order and number agreement. 
Although many high-level constituents can be 
"moved" in English, the lower-level structure of some 
constituents is relatively fixed. For example, after a 
determiner, one expects a noun rather than a verb. In 
this paper we also wish to ask, "How might this low-level 
fixed order assist in the resolution of ambiguity?" We 
will not give a definite answer to this question, but will 
see that it is extremely useful in the resolution of ambigu- 
ity. 
The examples of ambiguity shown in this paper seem 
to cause no apparent problems to a person reading them. 
That is, all of these examples read easily and certainly do 
not exhibit the garden path effect, except, of course, the 
examples that are intended to be difficult. If a parser is 
to be psychologically plausible, then it is desirable that it 
handle these examples in such a way as to explain why 
people have no apparent difficulty with most sentences, 
despite the inherent ambiguity in them. 
In parsing English, one of the major causes of non- 
determinism is part-of-speech ambiguity. If a word can 
be two parts of speech, then a non-deterministic parser 
may have to explore both possibilities. If one claims to 
be able to parse English deterministically, then the reso- 
lution of part-of-speech ambiguity is a very important 
area. 
It should be noted that a non-deterministic parser does 
not need to tackle the problem of local part-of-speech 
ambiguity. If it should make an error, then it can back- 
track and correct it. Alternatively, it could maintain all 
possible parses at once and throw some of them away. 
In deterministic parsing we are not allowed to use either 
backtracking or parallelism. Although this problem has 
been investigated for many non-deterministic parsers, it 
has not been the critical problem that it is for determinis- 
tic parsing. To handle ambiguity deterministically, we 
must never make an error. As a result, our methods of 
disambiguation must be reliable. We will see that many 
cases of ambiguity can be resolved using standard tech- 
niques that have been applied to non-deterministic 
parsers. 
If it is possible to handle all the examples of local 
ambiguity presented here, with no additional mechanism, 
device or feature than is needed for ordinary sentence 
parsing, then our goal above can be considered met. One 
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Computational Linguistics, Volume 12, Number 1, January-March 1986 1 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
possible explanation for the fact that people do not 
notice local ambiguities may be that there is no special 
mechanism needed for them, so that nothing differing 
from normal parsing is necessary. 
Conversely, if it is necessary to add special mech- 
anisms and routines to the parser just to handle these 
examples of ambiguity, then this will not explain how 
people can understand these examples so well and it can 
be considered a weakness in the model. 
To say part-of-speech ambiguity can be handled 
deterministically but with the use of special mechanisms 
would be no surprise and not very important. To say one 
can handle part-of-speech ambiguity deterministically 
with no special mechanisms is a more significant claim. 
In this paper it is indeed suggested that many cases of 
part-of-speech ambiguity can be handled by the parser 
with no special mechanisms. 
This paper is a summary of a section of the author's 
Ph.D. thesis (Milne 1983) with the same title and 
describes work done at the University of Edinburgh. 
That thesis presents ROBIE, a deterministic parser that is 
able to resolve lexical ambiguities and that is fully imple- 
mented in PROLOG. ROBIE has two lookahead buffers 
and does not use Marcus's Attention Shift mechanism. 
This means that ROBIE scans the current token and one 
more of lookahead. PARSIFAL scanned the current 
token and two lookahead cells. In this paper, only local 
ambiguities are addressed, that is, ambiguities that can be 
resolved within the sentence. Global ambiguities, which 
require context to resolve, are not discussed. For this 
paper, it is assumed that the reader is familiar with deter- 
ministic parsing and no other understanding of specific 
parsing mechanisms is assumed. 
In the rest of this paper, we look at lexical ambiguity 
from simple examples to more complex ones. We start 
with how words are defined within the parser to be 
ambiguous and how the morphology can be used to 
resolve ambiguities. Next we look at how word order 
and finally various types of agreement can be used to 
resolve most remaining ambiguities. 
2 SYNTACTIC CONTEXT 
2.1 WORD DATA STRUCTURES 
As a first approach to handling ambiguity, it was asked, 
"If we construct a compound lexical entry for each word 
composed of the features of each part of speech the word 
can have and make no alterations to the grammar, how 
wide a coverage of examples will we get?" 
This approach was used by Winograd (1972) and was 
found to be very effective for the following reason. Each 
word has all the possible relevant features for it. There- 
fore, the test will succeed for each possible part of speech 
with which a word can be used. In this way, all applica- 
ble rules will match. It may be that often only one rule 
will match, or that the first rule tried is the correct rule. 
The question is, how often will the rule that matches be 
the correct rule? 
All words in ROBIE are defined in the syntactic 
dictionaries. Each word has a compound lexical entry 
incorporating all the features for all the possible parts of 
speech the word could have. This is exactly as was done 
by Winograd (1972). For example, block is defined as a 
noun and a verb, can is defined as a noun, auxiliary verb, 
and verb, and hit is defined as a noun and a verb. The 
features for each of these parts of speech are kept in the 
dictionary and, when the word is looked up, they are 
returned as a single ordered list of features. These 
features are sub-grouped according to the part of speech 
they are associated with. Hence, when the word block is 
looked up, the result returned is both the noun and the 
verb definition. In this way, all possibilities are returned. 
In the English language, most words can have several 
parts of speech. This fact must be reflected in a parser of 
English and we do this with the multiple meanings above. 
When the parser has enough information to decide which 
is the correct part of speech, it ignores (removes) the 
other possibilities. In this way, we have not built struc- 
ture that is later thrown away. Although some may argue 
that this is a form of parallelism, it seems necessary since 
it reflects the inherent parallelism of language. 
2.2 MORPHOLOGY 
The first part of the disambiguation process takes place 
in the morphology. When ROBIE identifies a word that 
has a morphological ending, the morphology must adjust 
the features of the word. For example, when blocked is 
identified, the feature "ed" must be added to the list of 
features for block. At the same time, a portion of the 
disambiguation takes place. If block is defined as both a 
noun and a verb, then blocked is not a noun. The 
morphology causes some features to be added, such as 
"ed, past" and some features to be removed such as 
"tenseless." As features that are no longer applicable are 
removed, so also are parts of speech and their associated 
features that are no longer applicable. For blocked, the 
features "noun, ns, n3p" will be removed and the 
features "adjective, ed, past" will be added. 
The morphology will identify words such as adverbs, 
adjectives, and verbs in a similar way. The morphology 
used is very similar to that of Winograd (1972) and of 
Dewar, Bratley, and Thorne (1969); the part-of-speech 
additions and deletions are taken from Marcus (1980). 
Although this technique may seem obvious, it is included 
to point out that a majority of the occurrences of part-of- 
speech ambiguity can be resolved or reduced on the basis 
of the morphology alone. 
2.3 DISAMBIGUATION 
Now that we have allowed words to have multiple parts 
of speech and the morphology can be used to trim some 
of the ambiguity, we need a simple technique for disam- 
biguating words to a single part of speech. Again, refer- 
ring to Occam's Razor, what is preferable is a simple and 
general technique for all types of disambiguation. 
2 Computational Linguistics, Volume 12, Number 1, January-March 1986 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
In ROBIE each rule matches the features of one or two 
buffer cells. (The word buffer will be used interchangea- 
bly with cell. That is, buffer and cell are the same 
concept.) If the word block is in the first buffer cell, then 
a pattern \[noun\] or a pattern \[verb\] will match. These 
patterns do not relate to the other possible definitions of 
a word. If a rule pattern has matched on the feature 
"noun" in the first buffer cell, then ROBIE assumes that 
this word is a noun. It would then be appropriate to 
disambiguate the word as a noun. This is exactly as in 
Winograd (1972). 
In a non-deterministic parser, it is not essential to find 
the correct rule first. If the parser runs an incorrect rule, 
the parser may backtrack and change the category 
assignment. But in a deterministic parser, there will 
never be any backtracking, and this solution cannot be 
used. 
Since ROBIE does not backtrack, disambiguating the 
word when the pattern matches will always result in the 
same disambiguation as if the word were disambiguated 
in the grammar rule. Once a rule runs assuming a buffer 
contains a certain part of speech, it must be used as such 
in the parser. The general disambiguation scheme is: if a 
full pattern matches a word as a certain part of speech, 
then it is disambiguated as that part of speech. 
The compound lexical entries and pattern-matching 
disambiguation alone will handle many examples of 
ambiguity. In the rest of this paper we see just what this 
can do for us. 
2.4 AN EXAMPLE 
Given the above mechanisms - multiple definition and 
disambiguation by the pattern matching, let us see how a 
few simple examples are handled. Consider: 
(1) The falling block needs painting. 
We will look only at the words falling and block in this 
example. The word falling is defined as a verb and an 
adjective in the dictionary and block is defined as a noun 
and a verb. 
While parsing this example, after the word the has 
initiated an NP and been attached to it as a determiner, 
the rules to parse adjectives are activated. The rule 
ADJECTIVE has the pattern \[adj\], and matches the word 
falling. Falling is then attached and disambiguated as an 
adjective. Recognition of falling as a verb does not 
occur. As there are no more adjectives, ROBIE will acti- 
vate the rules to parse the headnoun. (ROBIE's grammar 
assumes that all words between the first noun and the 
head noun of an NP are nouns; see section 2.6.) The rule 
NOUN with the pattern \[noun\] will match on the word 
block, and it will be attached as a noun. Hence block will 
also be disambiguated without the verb use being consid- 
ered by ROBIE. 
Other ambiguities inside the noun phrase will be 
handled in a similar way. This approach will usually 
cover the situation of singular head nouns, 
verb/adjective ambiguity and many other pre-nominal 
ambiguities. This works because the noun phrase has a 
very strict word order. When an ambiguous word is 
found, only one of its meanings will be appropriate to the 
word order of the noun phrase at that point. This 
approach can be thought of as an extension of the basic 
approach of the Harvard Predictive Analyzer (Kuno 
1965). 
This strategy will also often disambiguate main verbs. 
For example, consider the following sentences: 
(2) Tom hit Mary. 
(3) Tom will hit Mary. 
(4) The will gave the money to Mary. 
In (2), hit is the main verb. In the dictionary, hit is 
also defined as a noun, (as in card playing). The parser 
will attach Tom as the subject of the sentence and then 
activate the rules for the main verb. Since hit has the 
feature "verb", it will match that rule and be attached 
and disambiguated as a verb. Again, other possible parts 
of speech are not considered. 
The word will could be a noun or a modal as sentences 
(3) and (4) demonstrate. In (3), will cannot be part of 
the headnoun with Tom, so the NP will be finished as 
above. The rules for the auxiliary will then be activated 
and the word will then matches the pattern \[modal\] and 
is attached to the AUX. 
In (4), the word will is used as a noun. Since it 
follows the determiner, the rules for nouns will be acti- 
vated. The word will then matches the pattern \[noun\] 
and attaches to the NP as a noun. 
The same approach will also disambiguate stop and run 
in the following sentence. Since stop is sentence initial 
and can be a tenseless verb, the rule IMPERATIVE will 
match, and it will be disambiguated as a verb. The word 
run, which can be a noun or a verb, will be handled as 
will in (4). 
(5) Stop the run. 
2.5 THE WORD TO 
NOW let us consider a more difficult example, the word 
to. To is defined as an auxiliary verb and a preposition in 
ROBIE, as illustrated by these sentences: 
(6) I want to kiss you. 
(7) I will go to the show with you. 
In (6), to is the infinitive auxiliary, while in (7) to is a 
preposition. This analysis is based on that of Marcus 
(1980:118). Our two buffer cell lookahead is sufficient 
to disambiguate these examples. 
The buffer patterns for the above sentences are: 
\[to&tenseless\] -~ embedded VP 
\[to&ngstart\] -~ PP 
By looking at the following word, to can be disambigu- 
ated. In (7), the word the cannot be a tenseless verb, so 
the first pattern does not match. In (6), the second buff- 
Computational Linguistics, Volume 12, Number 1, January-March 1986 3 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
er does not have the feature "ngstart", so the rule doesn't 
match. 
However, the above patterns will accept ungrammat- 
ical sentences. To reject ungrammatical sentences, we 
can use verb subcategorisation as a supplement to the 
above rules. One cannot say: 
(8) *I want to the school with you. 
(9) *I will hit to wash you. 
In English, only certain verbs can take infinitive 
complements. To can only be used as an auxiliary verb 
starting a VP when the verb can take an infinitive 
complement. Hence, by activating the rules to handle the 
VP usage only when the infinitive is allowed, the problem 
is partly reduced. Also by classifying the verb for PPs 
with the preposition to, the problem is simplified. This is 
merely taking advantage of subcategorisation in verb 
phrases. Taking advantage of this subcategorisation 
greatly reduces, but does not eliminate, the possible 
conflict. 
We have seen what to do if the verb will only accept a 
toPP or a VP. The final difficult situation arises whenev- 
er the following three conditions are true: 
• the verb will accept a toPP and a toVP, 
• the item in the second buffer has the features 
"tenseless" and "ngstart" and, 
• the toPP is a required modifier of the verb. 
Although this situation rarely arises, the above rule will 
make the wrong decision if the ambiguous word is being 
used as a noun. In this situation, ROBIE will make the 
wrong decision, and has no capability to better decide. 
By default, the principles of Right Association and Mini- 
mal Attachment apply as discussed in Frazier and Fodor 
(1978). 
A free text analysis done on a cover story in TIME 
magazine (1978) resulted in 55 occurrences of the word 
to. The two rules mentioned above in conjunction with 
verb subcategorisation gave the correct interpretation of 
all of these. These rules were also checked on the 
MECHO corpus (Milne 1983) and the ASHOK corpus 
(Martin, Church, and Patil 1981). There were no 
violations of these rules in either of these. 
2.6 ADJECTIVE/NOUN AND NOUN/NOUN AMBIGUITY 
Adjective/noun ambiguity is beyond the present scope of 
this research and is handled in a simple-minded way. If 
the word following the ambiguous adjective/noun word 
can be a noun, then the ambiguous word is used as an 
adjective. In other words, all conflicts are resolved in 
favour of the adjective usage. This problem arises in 
these examples: 
(10) The plane is inclined at an angle of 30 degrees 
above the horizontal. 
(11) A block rests on a smooth horizontal table. 
In (10), horizontal is a noun, while in (11), it is an 
adjective. The above algorithm handles these cases. 
This approach takes advantage of the lookahead of the 
deterministic parser. A word should be used as an adjec- 
tive if the following word can be an adjective or a noun. 
However, this approach would fail on examples such as: 
(12) The old can get in for half price. 
(13) The large student residence blocks my view. 
2.7 WHY DO THESE TECHNIQUES WORK? 
In this section we have seen many examples of the reso- 
lution of ambiguity. To handle these examples, we mere- 
ly constructed a compound lexical entry for each word, 
composed of the features of each part of speech the word 
could be and allowed the pattern matching to perform 
the disambiguation. This technique has been used by 
Winograd (1972). Why does this work so well? 
English has a fairly strict structural order for all the 
examples presented here. Because of this, in each exam- 
ple we have seen, the use of the word as a different part 
of speech would be ungrammatical. Although these tech- 
niques have been used for non-deterministic parsers, 
their effectiveness has not been investigated for a deter- 
ministic parser. 
Most ambiguities are not recognised by people 
because only one of the alternatives is grammatical. In 
many situations, when fixed constituent structure is taken 
into account, other uses of an ambiguous word are not 
possible and probably not even recognised. Since fixed 
constituent structure rules out most alternatives, we have 
been able to handle the examples in this paper without 
any special mechanisms. In the introduction to this 
paper, it was stated that a clean and simple method of 
handling ambiguity was desired. I feel that this goal has 
been met for these examples. 
3 THE ROLE OF AGREEMENT IN HANDLING AMBIGUITY 
Using the simple techniques presented in the last 
sections, we can handle many cases of part-of-speech 
ambiguity, but there are many examples we cannot 
resolve. For example, the second of each pair of 
sentences below would be disambiguated incorrectly. 
(14) I know that boy is bad. 
(15) I know that boys are bad. 
(16) What boy did it? 
(17) What boys do is not my business. 
(18) The trash can be smelly. 
(19) The trash can was smelly. 
Many people wonder what role person/number codes 
and the relatively rigid constituent structure in the verb 
group play in English. In this section, we will explore 
their role by attempting to answer the question, "What 
use is the fixed structure of the verb group and 
person/number codes?" 
4 Computational Linguistics, Volume 12, Number 1, January-March 1986 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
3.1 UNGRAMMATICAL SENTENCES 
Before we proceed, let us look at an assumption Marcus 
made in his parser, that it would be given only grammat- 
ical sentences. This assumption makes life easy for 
someone writing a grammar, since there is no need to 
worry about grammatical checking. Hence no provision 
was made for ungrammatical sentences and the original 
parser accepted such examples as: 
(20) *A blocks are red. 
(21) *The boy hit the girl the boy the girl. 
(22) *Are the boy run? 
This simplification causes no problems in most 
sentences, but can lead to trouble in more difficult exam- 
ples. If the parser's grammar is loosely formulated 
because it assumes it will be given grammatical examples 
only, then ungrammatical sentences may be accepted. If 
the syntactic analysis accepts ungrammatical sentences as 
grammatical, then it is making an error. Using grammat- 
ical constraints actually helps parsing efficiency and 
disambiguation. In the next sections we look at the 
consequences of this assumption as well as those of 
rejecting ungrammatical sentences. 
3.2 SUBJECT/VERB AGREEMENT 
We know that the verb group has a complicated but rela- 
tively fixed constituent structure. Although verbals have 
many forms, they must be mixed in a certain rigid order. 
We also know that the first finite verbal element must 
agree with the subject in person and number. That is, 
one cannot say: 
(23) *The boy are run. 
(24) *The boy will had been run. 
(25) *The boys had are red. 
etc. 
While Marcus's parser enforced these observations to 
some extent, he did not follow them throughout his 
parser. We want to enforce this agreement throughout 
ROBIE. Checking the finite or main verb, to be sure that 
it agrees in number with the subject, will lead to the 
rejection of the above examples. This was done by 
adding the agreement requirement into the pattern for 
each relevant rule as will be explained later. 
Buffers 1 and 2 must agree before a rule relating the 
subject and verb can match. This check looks at the 
number code of the NP and the person/number code of 
the verb and checks whether they agree. The routine for 
subject/verb agreement is very general and is used by all 
the subject/verb rules. The routine can only check the 
grammatical features of the buffers. 
3.3 MARCUS'S DIAGNOSTICS 
Marcus (1980) did handle some part-of-speech ambigui- 
ties. The words to, what, which, that, and have could all 
be used as several parts of speech. For each of these 
words he also used a Diagnostic rule. These Diagnostic 
rules matched when the word they were to diagnose 
arrived in the first buffer position and the appropriate 
packets were active. Each diagnostic would examine the 
features of the three buffers cells and the contents of the 
Active Node Stack. Once the diagnostic decided which 
part of speech the word was being used as, it either 
added the appropriate features, or explicitly ran a gram- 
mar rule. Marcus did not give each word a compound 
lexical entry as we have done here. 
Most of the grammar rules in his parser were simple 
and elegant, but the diagnostics tended to be very 
complex and contained many conditionals. In some cases 
they also seemed rather ad hoc and did not meet the goal 
of a simple, elegant method of handling ambiguity. 
For example, consider the THAT-DIAGNOSTIC: 
\[that\]\[np\] -* in the Packet CPOOL (Clause pool of rules) 
"If there is no determiner of second 
and there is not a qp of second 
and the nbar of 2nd is none of massn,npl 
and 2nd is not-modifiable 
then attach as det 
else if c is nbar 
then label 1 st pronoun, relative pronoun 
else label 1st complementiser." 
(Marcus 1980:291) 
Notice that if the word that were to be used as a deter- 
miner, then it would be attached after the NP was built! 
This is his primary rule for disambiguating the word that. 
Marcus's parser also had three other rules to handle 
different cases. 
It seems that these rules did not "elegantly capture 
generalisations" as did the rest of his parser. I consider 
these rules undesirable and feel that they should be 
corrected to comply with my criteria for simple and 
elegant techniques in resolving ambiguity. I wanted a 
method that used no special mechanism, or routine, other 
than that needed to parse grammatical sentences. These 
diagnostics are certainly special mechanisms and do not 
meet this goal. Can we cover the same examples in a 
more simple and principled way? 
In this section, we look at each of these diagnostics in 
turn and show how they have been replaced in the newer 
model. We also look at a few other examples of ambigui- 
ty which Marcus did not handle, but are related to our 
discussion here. 
3.4 HANDLING THE WORD TO 
The handling of to by Marcus's diagnostic can be 
replaced by the method outlined in Section 2.5. This 
method was motivated to handle grammatical sentences 
and meets our criterion for a simple approach. 
3.5 HANDLING WHATAND WHICH 
For both what and which, the ambiguity lies between a 
relative pronoun and a determiner. The following exam- 
ples show various uses of both words: 
Computational Linguistics, Volume 12, Number 1, January-March 1986 5 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
(26) Which boy wants a fish? det 
(27) Which boys want fish? det 
(28) The river which I saw has many fish. tel. pron. 
(29) What boy wants a fish? det 
(30) What boys want is fish. tel. pron. 
There is some debate about the part of speech to be 
assigned the word which. Some linguists consider it to be 
a quantifier (Chomsky 1965), while others consider it to 
be a determiner (Akmajian and Heny 1975, Chapter 8). 
We shall adopt the determiner analysis, making the prob- 
lems for what and which similar. 
To determine the correct part of speech for these two 
words, Marcus (1980:286) used the following diagnos- 
tics: 
\[which\] -~ in the packet CPOOL 
"If the NP above Current Node is not modified 
then label 1st pronoun, relative pronoun 
else label 1st quant,ngstart,ns,wh,npl." 
\[what\]\[t\] -*- in the packet NPOOL 
"If 2nd is ngstart and 2nd is not det 
then label 1st det,ns,npl,n3p,wh; 
activate parse det 
else label 1st pronoun,relpron,wh." 
These diagnostics would make the word in question a 
relative pronoun if it occurred after a headnoun, or a 
determiner if the word occurred at the start of a possible 
noun phrase. 
If we follow the approach in the last section, and give 
each word a compound lexical entry composed of the 
determiner and relative pronoun features, we find that 
these words are always made determiners unless they 
occur immediately after a headnoun. In other words, the 
which examples are all parsed correctly, but (30) is 
parsed incorrectly. This happens because the determiner 
rule will always try to match before the rule for WH ques- 
tions can take effect. This simple step gives the correct 
analysis if the ambiguous word is to be a determiner, but 
will still err on (30). 
The rule to parse a relative pronoun and start a rela- 
tive clause is active only after the headnoun has been 
found. At this time, the rule for determiners is not 
active. Therefore, if the word what or which is present 
after a headnoun, the only rule that can match is the rule 
to use it as a relative pronoun, and it will be used as a 
relative pronoun. We have resolved the simple case of 
what as a relative pronoun using only the simple tech- 
niques of the last section. For these sentences 
(31) What block is red? 
(32) Which boy hit her? 
(33) Which is the right one? 
ROBIE produces the correct analysis, but still errs on 
(30). This error is because what is being used as a rela- 
tive pronoun but does not follow a headnoun. Without 
any additional changes to the parser, we get two things. 
Firstly, if the word occurs after the headnoun, then the 
NP-COMPLETE packet rules are active, and it will be a 
relative pronoun. In fact, since relative clauses can occur 
only after the end of an NP, this correctly resolves the 
relative pronoun uses. If the word occurs at the start of 
an NP, then it will be made a determiner. 
This approach has exactly the same effect and cover- 
age as did Marcus's diagnostics, but we have not needed 
any special rules to implement it. It will now provide the 
correct interpretation for which, but will make some 
errors for the word what. Marcus's what-diagnostic will 
treat what as a determiner whenever the item in the 
second buffer could start a NP. This is usually correct, 
but what will be treated as a determiner in all of the 
following: 
(34) What boys want is fish. 
(35) What blocks the road? 
(36) What climbs trees? 
(37) What boys did you see? 
(38) What blocks are in the road? 
(39) What climbs did you do? 
In this paper, we are adopting the following analysis 
for WH clefts such as (34). The initial WH word, what is 
a relative pronoun and attached as the WH-COMP of the 
subject S node. The subject is the phrase What boys 
want. The main verb of the sentence is is and the 
complement fish. The exact details are not important, 
only that the word what or which is a not determiner at 
the start of a WH cleft. 
In sentences (34-36), the word what is not used as a 
determiner. In the analysis we are using, it is a relative 
pronoun and is used as the WH-COMP for the S. In 
sentences (37-39), the word what is used as a determiner. 
Marcus (1980:286) admits that this diagnostic produces 
the incorrect result in this case. His diagnostic will make 
what a determiner in all of these examples, as will my 
analysis. 
One can also see that each of the above pairs is a pair 
of potential garden path sentences. For each pair, the 
two buffers contain the same words. Hence our two-buf- 
fer lookahead is not sufficient to choose the correct usage 
of the word what. Using only two or three buffers, there 
is no way to make what a relative pronoun when the 
headnoun is plural but a determiner when it is singular 
for all arbitrary sentences. 
With regard to the Semantic Checking Hypothesis 
(Milne 1982) then, it is suggested that this decision is 
based on non-syntactic information. I believe that into- 
nation is critical in these examples. Unfortunately there 
is insufficient experimental evidence to determine for 
certain whether this is true. Finally, the problem of what 
and which as sentence initials, with no noun in the second 
buffer seems to arise very rarely. I have found no exam- 
ples of this problem in free text analysis. 
The current parser (ROBIE) cannot obtain the extra 
information provided by intonation to help resolve this 
case. As a result it follows Mareus's diagnostic and 
makes what a determiner in each of the above cases. 
6 Computational Linguistics, Volume 12, Number 1, January-March 1986 
Robert Milne Resolving Lexieal Ambiguity in a Deterministic Parser 
This is because what is defined as a determiner that can 
agree with either a singular noun or a plural noun, as it 
was in Marcus's parser. 
3.6 HANDLING THAT 
In ROBIE, that is defined as a singular determiner, a 
pronoun, a relative pronoun, and a complementiser. 
Marcus had four diagnostics to handle the word that. We 
have seen one of these at the start of this section. In this 
sub-section we see how these four diagnostics can be 
replaced in a simple way. Let us consider how to handle 
the uses of that one at a time. 
Firstly, as a determiner. The following sentences illus- 
trate the problem in identifying this usage. 
(40) I know that boy should do it. 
(41) I know that boys should do it. 
Marcus assumed that PARSIFAL would be given only 
grammatical sentences to parse. If determiner/number 
agreement is not given to a parser, then it will, incorrect- 
ly, make that a determiner in (41), producing the wrong 
analysis. The way to prevent this is to enforce number 
agreement in the rule DETERMINER by insisting that the 
determiner agree with the noun in number. The deter- 
miner usage will be grammatical only when the headnoun 
has the same number. If we make this a condition for the 
rule to match, then that will not be made a determiner in 
(41) and ROBIE will get the correct parse. 
For this case, the agreement check would make sure 
that one of the following patterns match: 
\[det,ns\] \[noun,ns\] 
\[det,npl\] \[noun,npl\] 
The above two cases are handled properly because 
number agreement blocks the interpretation of the (41) 
as a determiner. This approach leads to the correct pref- 
erence, when there is an ambiguity and accounts for the 
difficulty in (42) versus (43): 
(42) That deer ate everything in my garden surprised 
me. 
(43) That deer ate everything in my garden last night. 
The second experiment in Milne (1983), showed that 
(42) is a garden path sentence, while (43) is not. In both 
sentences, it is believed the subject uses the word that as 
a determiner. Deer is both singular and plural, so it fits 
the above rule. In (42), that must be used as a complem- 
entiser to make the sentence grammatical. The approach 
outlined above will use that as a determiner in an ambig- 
uous case such as this. 
These two simple techniques, word order and agree- 
ment, are sufficient to handle all the examples we have 
just presented. In addition, free text analysis has shown 
no violations to this approach (Milne 1983). These tech- 
niques provide the same coverage as Marcus's diagnostic, 
with the added bonus that the determiner is attached 
before the NP is built. 
That can only be a complementiser when a that S- is 
expected. Hence the rules using that to start an embed- 
ded sentence are only activated when the verb has the 
feature THAT-COMP. The rules in THAT-COMP will fire 
when that is followed by something that can start an NP. 
This ensures that the S- will have a subject and means 
that that will be taken as a pronoun in the following 
sentences: 
(44) I know that hit Mary. 
(45) I know that will be true. 
but it will be taken as a complementiser in these 
sentences: 
(46) I know that boys are mean. 
(47) I know that Tom will hit Mary. 
It seems that, unless the S- has a subject, the pronoun 
use of that is preferred. Otherwise one would have a 
complementiser followed by a trace, rather than a 
unmarked complementiser, followed by a pronoun. This 
rule provides more complete coverage than Marcus's 
diagnostic since it examines the second buffer. 
The rule to handle pronouns in general is of low prior- 
ity and will only fire after all other uses have failed to 
match. That is treated in the same way. 
That will be identified as a relative pronoun only if it 
occurs after a headnoun and the packet NP-COMPLETE 
is active. This situation will be handled in the same 
manner as the usual relative clause rules and will then 
cover: 
(48) I know the boy that you saw. 
(49) I know the boy that hit you. 
The most difficult case for that is when the verb is 
subcategorised: 
VNPS- 
That is, it can take an NP subject, followed by a that S-. 
For these examples, ROBIE may have to decide if the 
series of words following that is a relative clause or an 
embedded sentence. 
In the following sentences, the lookahead would have 
to be more than three buffers. (Brackets indicate words 
in the buffers. The last word is the disambiguating 
word.) 
(50) I told the girl \[that\]\[the\]\[boy\] hit the story 
(51) I told the girl \[that\]\[the\]\[boy\] will kiss her 
It can be seen that in these sentences the disambiguat- 
ing word is outside our three buffers. How do people 
handle these, and what should our parser do? In Milne 
(1983) it was shown that when the syntax could not 
resolve the ambiguity with its two-buffer lookahead, the 
decision of which interpretation to use might be made 
using non-syntactic information. It was also stated that if 
context can affect the interpretation of the sentence, then 
non-syntactic information is being used to select the 
Computational Linguistics, Volume 12, Number 1, January-March 1986 7 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
interpretation. The reader can experiment for himself 
and see that context does affect the interpretation of 
these sentences. Therefore it is predicted that non-syn- 
tactic information is being used to interpret these 
sentences, and that this problem should be resolved not 
on a semantic basis but on a non-syntactic one. 
This explains why some of these examples cause diffi- 
culty and others do not. The psychological evidence 
from cases using that is scant, and I feel no conclusions 
can be reached here. My theory predicts that context 
will strongly affect these examples and, if they are 
strongly biased to the incorrect reading, a garden path 
should result. 
One well-known example in this area is (52): 
(52) I told the girl that I liked the story. 
(53) I told the girl whom I liked the story. 
(54) I told the girl the story that I liked. 
These examples were tested in Milne (1983). The results 
suggested that (52) was read faster than the other two 
examples. Many of the subjects were questioned 
informally after the experiment about their interpretation 
of the sentence. All reported only one meaning; the S- 
reading. None of the subjects said that they noticed the 
relative clause reading, hence the result. The experiment 
however, was not designed formally to distinguish these. 
To handle the examples we have seen in this section, 
Marcus had four diagnostics, one of which was very 
complicated. I have just shown how to handle all four 
cases of that without any special rules, merely substitut- 
ing enforced agreement and rejecting ungrammatical 
sentences. 
3.7 HANDLING THE WORD HA VE 
Let us now look at the elimination of Marcus's 
HAVE-DIAGNOSTIC in relation to the use of agreement 
we have been discussing in this section. The problem 
with have is illustrated by the following sentences: 
(55) Have the students take the exam. 
(56) Have the students taken the exam? 
In these, we must decide if have is an auxiliary verb or a 
main verb and whether the sentence is a yes-no question 
or an imperative. The sentences have the same initial 
string until the final morpheme on take. To handle this 
case, Marcus (1980:211) used this rule: 
"RULE HAVE-DIAG PRIORITY:5 IN SS-START 
\[have,tenseless\]\[np\]\[t\] -~ 
If 2nd is ns,n3p or 3rd is tenseless 
then run imperative next else 
If 3rd is not verb 
then run yes-no-question next 
else if not sure, assume it's a y/n-q and run yes-no- 
question next"." 
This rule seems to be necessary in order to distinguish 
between the question and the imperative. If one tries to 
ascertain exactly what occurs, the apparent complexity is 
revealed. Note also that Marcus's rule defaults to a yes- 
no question twice in this diagnostic. The following 
sentences illustrate the distinction this rule makes. 
(57) Have the boy take the exam. 
(58) Have the boy taken the exam. 
(59) Have the boys take the exam. 
(60) Have the boys taken the exam? 
It can be seen that YES-NO QUESTION should run 
only when the NP following is plural and the verb has 
"en" (i.e., taken). \[Only (60) has a plural noun, the boys, 
and the verb taken.\] This can also be understood as: the 
sentence is an imperative if the item in the 2nd buffer is 
not plural and the verb is tenseless. Thus, the first three 
examples above are Imperatives because either the noun 
(boy) is singular (57 and 58) or the verb is tenseless (59). 
The second part of the rule takes care of the fact that the 
third buffer must contain a verb for the imperative, as 
this would be the main verb of the embedded sentential 
object. 
Let us 10ok more closely at the reason why only (60) 
is a question. Firstly, if the sentence is a yes-no question, 
then aux-inversion must occur. When this happens, Have 
will be adjacent to the verb that was in the third buffer. 
In order for ROBIE to continue, the verb must have an 
"en" ending, or have and the next verb will not agree in 
aspect. This is the basis for discrimination in the earlier 
examples (57-60). 
Secondly, in (57) and (58), the noun phrases are 
singular and both sentences are imperatives. Had the 
sentence been a yes-no question, have would need to 
agree with the subject, which must then be plural. 
Hence, in effect, Marcus's rule checks for number 
agreement between the subject and verb, and checks that 
the fixed order of the verb group is obeyed. Let us now 
look at other situations where this is necessary. 
PARSIFAL would accept the following ungrammatical 
strings: 
(61) *Are the boy running? 
(62) *Has the boys run? 
(63) *Has the boy kissing? 
(64) *Has the boy kiss? 
For a yes-no question, the inverted auxiliary must 
agree with the verb after it has been inverted. To stop 
these ungrammatical constructions, we must enforce verb 
agreement. The pattern for the rule YES-NO QUESTION 
should be: 
\[auxverb\]\[np\]\[verb\], agree(auxverb,verb),agree(verb,np). 
This constraint enforces agreement of the verb and 
auxiliary verb and the subject and verb. Again this check 
is based only on the linguistic features of the buffers. 
Such a constraint effectively blocks the ungrammatical 
constructions. (The parser will fail if the auxiliary has 
been inverted, since the auxiliary will not be parsed.) 
Also the subject NP must agree with the auxiliary verb, 
so we can also add "agree(auxverb,np)" to the rule, as 
8 Computational Linguistics, Volume 12, Number 1, January-March 1986 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
we did with the HAVE-DIAGNOSTIC! So, by correcting 
the yes-no question rule, the HAVE-DIAGNOSTIC is 
redundant. 
In this section we have seen that Marcus's 
HAVE-DIAGNOSTIC can be replaced by merely exploit- 
ing agreement. It should be pointed out that although 
this approach has the same coverage as Marcus's diag- 
nostic, it is wrong in some cases. Milne (1983) has a full 
discussion. 
3.8 PLURAL HEAD NOUNS 
There is a class of ambiguities that can be resolved mere- 
ly by enforcing subject/verb agreement. In this section, 
we see an example from the class of words with the 
features noun, verb, final-s (plural). If we have two 
words that can be a plural noun or a singular verb, we 
can enumerate four cases. Let us look at these possibil- 
ities and see that these cases can be disambiguated by 
simple rules using subject/verb agreement. The follow- 
ing examples illustrate all the possibilities: 
(65) The soup pot cover handle screw is red. 
(66) The soup pot cover handles screw tightly. 
(67) *The soup pot cover handles screws tightly. 
{68} The soup pot cover handle screws tightly. 
{69} The soup pot cover handle screws are red. 
Each of the words pot, cover, handle, and screw can be 
either a noun or a verb. The "end of constituent" prob- 
lem is to find out which word is used as the verb and 
which words make up the complex headnoun. The possi- 
ble distributions of the morpheme "s" among two words 
gives us four cases. We deal with each of these in turn. 
Case 1: In (65) each noun is singular. For this case all 
ambiguous words must be nouns and part of the head- 
noun. Due to subject/verb agreement, a singular noun 
must match a 3rd person singular (v3s) verb, i.e, one 
without the letter "s". This case excludes that possibility 
since none of the words have an "s" at the end. Hence 
they must all be nouns. 
Case 2: In (66) handles is a plural noun and each word 
before it must be a noun. When a singular noun/verb 
word follows handles, the word (screw) must be a verb 
and handles is the last of the headnouns. It is not possi- 
ble to use handles in this situation as a verb, and screw as 
a noun because of subject/verb agreement. 
Case 3: The examples in this case have two consec- 
utive plural nouns as in (67), where both words have 
noun/verb ambiguity. (Do not confuse plural "s" with 
possessive "'s"). 
When the first plural is a noun, then the second one 
can be a verb only if it is part of a different constituent. 
Examples of this are the following. (Sentences beginning 
with "?" are considered grammatical but unacceptable to 
most readers.) 
(70) ?The soup pot machine handles screws easily. 
(71) The soup pot machine handles screw easily. 
(72) Which years do you have costs figures for? 
(73) Do you have a count of the number of sales 
requests and the number of requests filled? 
\[(72) and (73) are from Martin, Church, and Patil 
(1981).\] 
Because there is a non-plural headnoun followed by a 
plural headnoun, this case is really a subset of Case 4. In 
general, the problems and issues for Case 4 dominate the 
resolution of this ambiguity. 
Case 4: Sentences (68) and (69) both have the same 
word initial string until after screws, but in (68) screws is 
a verb while in (69) screws is part of the headnoun. In 
this situation, where the final word in a series is plural, 
each word before it must be a noun. The word itself can 
be either a noun or a verb, depending on what follows. 
These can be recognised as a pair of potential garden 
path sentences, as discussed in Milne (1982). Therefore, 
this is the case to which the Semantic Checking Hypoth- 
esis applies and the predictions of Milne (1982) apply. 
In that paper, the idea of potential garden path 
sentences is presented. These are sentences that may or 
may not lead to a garden path. Each garden path 
sentence has a partner, which is similar but not a garden 
path. It is proposed that the decision as to how to 
resolve the ambiguity that may lead to a garden path 
should be made by semantics and not by syntax. This 
theory is called the Semantic Checking Hypothesis. For 
full details see Milne (1983). 
In this section, we have looked at resolving a simple 
case of noun/verb ambiguity. In order to resolve this 
ambiguity, it was necessary merely to exploit agreement 
between the subject and verb in number and person. 
Due to number and subject verb agreement, these 
facts have a linguistic base. They rely on the fact that a 
final "s" marks a plural noun but a singular verb. If the 
verb is v3s (verb agrees with a 3rd person, singular 
noun, as with the "s"), then the subject of the verb must 
be singular, or else the sentence is ungrammatical. This 
is why all the words before the v3s word must be nouns. 
If any of these words were used as a verb, then subject- 
verb agreement would be violated. This is why (67) is 
ungrammatical. If the verb is v-3s (agrees with any 
noun phrase except 3rd person, singular i.e., no "s"), 
then the subject cannot be singular. (65) has no plural 
subject and so cannot have a v-3s verb. In (66) handles 
provides a plural subject, so screw, which is v-3s, can 
agree. 
3.9 NOUN/MODAL AMBIGUITY 
We now consider noun/modal ambiguity as demon- 
strated by can and will. Both can be either a noun or a 
modal (i.e., could, should, would, can, will, might, etc.): 
(74) The trash can was taken out. 
(75) The trash can be taken out. 
(76) The paper will was destroyed. 
(77) The paper will be destroyed. 
Computational Linguistics, Volume 12, Number 1, January-March 1986 9 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
Each of these words is entered in the dictionary both 
as a noun and a modal. Due to agreement requirements, 
the modal/noun word can only be grammatically used as 
a modal if the word following it is a tenseless verb, i.e., 
the pattern: 
\[modal\]\[tenseless\] -~ modal usage 
applies. Handling noun/modal ambiguity can be quite 
easy; when the noun modal word appears in the first 
buffer one merely has to look at the contents of the 
second buffer to see if it contains a tenseless verb. This 
can be complicated, though, if the auxiliary is inverted or 
the sentence is an imperative. The following examples 
show how this can arise: 
(78) Let the paper will be read. 
(79) Will the paper can be re-used? 
In sentence (78) the fragment Let the paper implies 
that will can only be used as a noun, as the sentence 
already has one tensed verb. In the parser, the 
noun/modal word is first encountered inside the NP 
packets and the parser must decide whether to use the 
word as part of the headnoun or to leave it in the buffer 
to be used as a modal verb. These rules do not know 
whether a verb has been found previously. Hence, not 
all information from the sentence is used. If all the infor- 
mation is available at the time the noun/modal ambiguity 
is being resolved, these sentences would be unambiguous 
and people would have no trouble reading them. 
Subjects were asked to read the above examples in the 
second experiment presented in Milne (1982). The 
results showed convincingly that they are potential 
garden paths. Many naive readers had considerably 
more difficulty with them than with their more straight- 
forward counterparts. This was predicted for reasons 
explained below. 
This result seems surprising. If the subjects used all 
information available at the time the noun/modal word 
was encountered, then they should have had no trouble 
with these sentences. The fact that these are garden 
paths indicates that the readers did not use all the infor- 
mation available to them. Notice also that the ambiguity 
can be reformulated as: "Do we have the end of a noun 
phrase, or a complex headnoun?" 
We have already seen a case where people do not 
seem to use all the information available to them. In 
Milne (1983), several end-of-NP problems were 
presented that could lead to a garden path. In each of 
these, it was shown that the ambiguity was resolved on 
the basis of non-syntactic information, without regard to 
the following words in the sentence. In other words, we 
saw that the reader did not use all the information avail- 
able. There is one crucial difference though. In the 
previous cases, non-syntactic information was used 
because the syntactic processor with its limited lookahead 
was sometimes unable to choose the correct alternative. 
In this case, the information necessary has already been 
absorbed by the parser. 
This suggests that the choice of alternatives is made 
locally inside the NP parsing rules, without regard to 
information about the type of sentence being parsed. In 
other words, the two-buffer pattern applies regardless of 
the rest of the sentence. This assumes that a 
noun/modal word followed by a tenseless verb is being 
used as a modal. This is similar to Fodor, Bever, and 
Garrett's (1974) old canonical sentoid strategy: a 
bottom-up analysis that took every N-V combination as a 
new S. Let us look at why this might be true in the 
parser. 
When the parser starts to parse a NP, it creates a new 
NP node and pushes it to the bottom item of the Active 
Node Stack. This operation makes the NP node the 
Current Active Node and parsing of the old Current 
Active Node is suspended. If the parser is parsing an S 
node, for example at the start of the sentence, then work 
on this node will be suspended until the NP node has 
been completed and dropped into the buffer. 
In ROBIE, unlike PARSIFAL, the pattern matcher for 
the grammar rules is allowed only to inspect the gram- 
matical features of the two buffers. This means that the 
parser is unable to examine the contents of the Active 
Node Stack and, hence, the information that a tensed 
verb has already been found is unavailable to the NP 
parsing rules. This then suggests that the ambiguity will 
be resolved on the basis of local information only. 
It should be pointed out that although ROBIE does not 
examine the Active Node Stack, the current packet 
reflects its contents. For example, if the parser is parsing 
the major S node, the packet SS-VP will be active, but if 
the parser is parsing an embedded S node, the packet 
Embedded-S will be active. This information can be 
considered to provide local context to the parsing rules. 
This is the same as in PARSIFAL. 
This ambiguity is an end-of-NP problem and the 
choice of alternatives is made on the basis of limited and 
local information. This suggests that non-syntactic infor- 
mation may be used to resolve the ambiguity. There is 
one further possibility. The semantic choice mechanism 
is attempting to find the end of an NP. So far it has 
asked the question, "Can this item be part of the NP?" 
However, the end-of-NP problem can be reformulated as, 
"Is it better to use this as part of the NP, or'as the start of 
the verb group?" It is conceivable that the end of NP 
mechanism uses will as the start of the verb group in the 
majority of occurrences, hence leading to the apparent 
modal preference in these examples: 
(80) The trash can hit the wall. 
(81) The paper will hit the table. 
Due to lack of data, it is not clear exactly what people do 
in this situation and this would seem to provide an inter- 
esting area for further investigation. 
10 Computational Linguistics, Volume 12, Number 1, January-March 1986 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
3.10 WHAT ABOUT HER 
Another problem is the word her, which can be used as a 
pronoun or as a possessive pronoun. Note that we can 
say: 
(82) Tom kissed her. 
(83) Tom kissed her sister. 
Clearly in (82) her is a pronoun and in (83) her is a 
possessive determiner. When multiple part-of-speech 
definitions were added to ROBIE and the simple disam- 
biguation method used, ROBIE always made her a posses- 
sive determiner. 
This difficulty arose in Marcus's parser because the 
rule to start a NP was ordered before the rule to parse a 
pronoun. These rules were copied directly into ROBIE's 
grammar. Since the word her has both the features 
"ngstart" and "pronoun", it could match both rules. 
Unfortunately, as Marcus's rules were stated, it always 
matched the NP starting rule, and hence was used as a 
determiner by the parser. This indicates one problem 
that can arise in the writing of a parser grammar. 
To handle possessive determiners, PARSIFAL and 
ROBIE have a rule with the pattern: 
\[poss__np\] 
This rule will match a possessive pronoun after it has 
been made into an NP. It will also match any possessive 
NP, such as: the boy's or the boy's mother's. The rule then 
adds the feature "determiner" to the NP, making it eligi- 
ble for the NP starting rule. By degrading the possessive 
NPs to determiners, both parsers easily handle examples 
of left branching such as: 
(84) The boy's mother's brother is his uncle. 
Another problem arose in (82) because the possessive 
NP rule was not sufficiently constrained. It is possible to 
use her as a determiner only where the next word can be 
part of a noun phrase with that determiner. To enforce 
this, the second buffer is checked to be certain that its 
contents will take the determiner. Using this approach, 
her in (82) would not be converted to a possessive deter- 
miner. The rule DETERMINER can run only if the next 
item will "take a determiner". 
This check is made by the syntactic category of the 
following word, rather than by a specially marked 
feature. This check could be done by having a list of all 
the possible categories as the pattern of the second buff- 
er. As an implementation detail, this is in the form of an 
agreement check, merely to simplify this rule and to show 
its generality. 
The only remaining problem occurs when the verb can 
take one or more objects and the item after the word her 
can be either the second object, or an NP with her as a 
determiner. For example: 
(85) I took her grapes. 
(86) He saw her duck. 
(87) I gave her food for the dog. 
The examples presented above are all examples of 
global ambiguity, which is discussed in more detail in 
Milne (1983). In these cases the check of "Will-the next 
word take a determiner?", may or may not lead to the 
wrong analysis. This problem also interacts with the 
top-down component of verb phrase parsing and the 
semantic restrictions presented by it. 
The conflict between the determiner and possessive 
usage can be modelled as a conflict of rule priorities. If 
the possessive use is preferred, then this rule should 
match first. Conversely, if the object use is preferred, 
then the object rule should match first. Any error in 
reading these examples would be due to one rule having 
priority over the other, when the reverse should be the 
case. Finally, notice that with no help from either into- 
nation or context, either analysis is possible. That is, 
there is not enough information in the sentence to deter- 
mine a unique interpretation. 
We have now shown how to replace all the diagnostics 
Marcus used. In doing this, we enforced number and 
verb agreement on the rules before they could run. This 
was motivated to reject ungrammatical items, rather than 
for the handling of ambiguity. While there are still a few 
problems due to global ambiguity, the approach reported 
here has the same coverage as Marcus's diagnostics, and 
provides a better explanation of why people have trouble 
on certain sentences. 
4 POSSIBLE USES FOR AGREEMENT IN ENGLISH 
In this paper, we have seen several occurrences of ambi- 
guity, for each of which we have found a parallel situ- 
ation that could lead to acceptance of ungrammatical 
sentences by ROBIE. We then used person/number 
codes or the fixed structure of the verb group to block 
these unacceptable readings. Most of our ambiguity 
problems were also handled by this method. Although 
this has been used before with non-deterministic parsers, 
it was not obvious that it would provide enough informa- 
tion to enable deterministic parsing. 
Once person/number codes are taken into account, 
the number of potential ambiguous readings is dramat- 
ically reduced. In many cases, only one of the ambiguous 
possibilities was grammatical. It should be noted that 
there are a few difficult cases which we have not had 
time to describe in this paper; these are discussed in 
detail in Milne (1983). 
Marcus had a few rules to resolve part of speech ambi- 
guity, but they were ad hoc. We have seen that we can 
replace these rules very simply by merely exploiting 
agreement. 
In the introduction, it was stated that handling lexical 
ambiguity was a major test for deterministic parsing. In 
this paper we have seen that many cases of ambiguity can 
• be resolved in a simple way. This is possible because of 
the constraints imposed by number agreement and word 
order. In fact, many cases of the seemingly difficult 
Robert Milne Resolving Lexical Ambiguity in a Deterministic Parser 
problem of lexical ambiguity turn out-to be easily 
resolved in a deterministic parser, since the deterministic 
parser uses more information to make decisions. 

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