Anaphor resolution in unrestricted texts with partial parsing 
A. Ferr~indez; M. Palomar 
Dept. Languages and Information Systems 
Alicante University - Apt. 99 
03080 - Alicante - Spain 
antonio@dlsi.ua.es mpalomar@dlsi.ua.es 
L. Moreno 
Dept. Information Systems and 
Computation 
Valencia University of Technology 
lmoreno@dsic.upv.es 
Abstract 
In this paper we deal with several kinds of 
anaphora in unrestricted texts. These kinds of 
anaphora are pronominal references, surface- 
count anaphora and one-anaphora. In order to 
solve these anaphors we work on the output 
of a part-of-speech tagger, on which we 
automatically apply a partial parsing from the 
formalism: Slot Unification Grammar, which 
has been implemented in Prolog. We only use 
the following kinds of information: lexical 
(the lemma of each word), morphologic 
(person, number, gender) and syntactic. 
Finally we show the experimental results, and 
the restrictions and preferences that we have 
used for anaphor resolution with partial 
parsing. 
Introduction 
Nowadays there are two different approaches to 
anaphor resolution: integrated and alternative. 
The former is based on the integration of different 
kinds of knowledge (e.g. syntactic or semantic 
information) whereas the latter is based on 
statistical, neural networks or the principles of 
reasoning with uncertainty: e.g. Connoly (1994) 
and Mitkov (1997). 
Our system can be included into the first 
approach. In these integrated approaches the 
semantic and domain knowledge information is 
very expensive in relation to computational 
processing. As a consequence, current anaphor 
resolution implementations mainly rely on 
constraints and preference heuristics which 
employ information originated from 
morphosyntactic or shallow semantic analysis, 
e.g. in Baldwin (1997). These approaches, 
however, perform remarkably well. In Lappin and 
Leass (1994) it is described an algorithm for 
pronominal anaphor resolution with a high rate of 
correct analyses: 85%. This one operates 
primarily on syntactic information only. In 
Kennedy and Boguraev (1996) it is proposed an 
algorithm for anaphor resolution which is a 
modified and extended version of that developed 
by Lappin and Leass (1994). In contrast to that 
work, this algorithm does not require in-depth, 
full, syntactic parsing of text. The modifications 
enable the resolution process to work from the 
output of a POS tagger, enriched only with 
annotations of grammatical function of lexical 
items in the input text stream. The advantage of 
this algorithm is that anaphor resolution can be 
realized within NLP frameworks which do not -or 
cannot- employ robust and reliable parsing 
components. Quantitative evaluation shows the 
anaphor resolution algorithm described here to 
run at a rate of 75% accuracy. Our framework 
will allow us a similar approach to that of 
Kennedy and Boguraev (1996), but we will 
automatically get syntactic information from 
partial parsing. Moreover, our proposal will also 
be applied to other kinds of anaphors such as 
surface-count anaphora or one-anaphora. 
There are some other approaches that work on 
the output of a POS tagger, e.g. that of Mitkov 
and Stys (1997), in which it is proposed another 
knowledge-poor approach to resolving pronouns 
in technical manuals in both English and Polish. 
This approach is a modification of the reported in 
Mitkov (1997). Here, the knowledge is limited to 
a small noun phrase grammar, a list of terms and 
This paper has been supported by the CICYT number TIC97-0671-C02-01 / 02 
385 
a set of antecedent indicators (definiteness, 
giveness, term preference, lexical reiteration, ...). 
We will work in a similar way to this approach, 
since we use some of its antecedent indicators, 
but we automatically apply a partial parsing that 
allows us to deal with other kinds of anaphors as 
well as pronouns. 
In this work we are going to apply a partial 
parsing on the output of a POS tagger in order to 
solve anaphora problem. We will work over the 
corpus used within CRATER z. This corpus 
contains the International Telecommunications 
Union CCITT handbook, also known as The Blue 
Book, in English, French and Spanish versions. 
This corpus is the most important collection of 
telecommunication texts and contains 5M words, 
automatically tagged by the Spanish version of 
the Xerox tagger. We will use the system Slot 
Unification Grammar (SUG) in order to get a 
partial parsing on the output of this tagger. 
SUG is a logical formalism based on 
unification, which is an extension of Definite 
Clause Grammars (DCG). It is called Slot 
Unification Grammar due to the slot structures 
generated by the parser. SUG has been developed 
with the aim of extending DCG in order to 
facilitate the resolution of several Natural 
Language Processing (NLP) problems in a 
modular way. This system has been firstly 
proposed in Ferr~ndez (1997a), and it has been 
previously applied to anaphor resolution in 
Ferr~indez (1997b). 
We have used SUG instead of other well 
known formalisms such as Head Driven Phrase 
Structure Grammar (HPSG), Lexical Functional 
Grammar (LFG) or Slot Grammars (SG), because 
SUG allows a modular and computational 
treatment of NLP problems, and it facilitates its 
integration with a POS tagger. 
In the following section we will briefly 
describe SUG formalism in order to facilitate the 
undertanding of this paper. In section 2 we will 
propose a SUG grammar to accomplish the partial 
parsing of the unrestricted text and the interface 
to work with the output of the POS tagger. In 
section 3 we will explain the algorithm used to 
anaphor resolution and its constraints and 
2 http://138.87.135.33/-mdavies/roanoke.htm 
preferences. And, finally, in section 4 we will 
offer some figures of the evaluation of the 
system. 
1 Slot Unification Grammar 
In this section we will briefly describe SUG 
formalism. We will only show some of the 
capabilities of SUG in order to undertand this 
paper. For further details on SUG it is necessary 
to consult Femindez (1997a). 
SUG can be defined as this quadruple: 
(NT, T,P,H), where NT and T are a finite set of 
nonterminal and terminal symbols respectively; 
moreover NT~ T = fD. P is a finite set of pairs 
++> 13 where ot~NT, 13~(TuNT)*u {procedures 
calls}, and these pairs are called production rules. 
Finally H is a set of production rules which only 
has the first member of the production rule, i.e. a, 
and ot's name is either coordinated, juxtaposition, 
fusion, basicWord or isWord. 
SUG's production rules adds to those of DCG 
that each subconstituent of 13 could be omitted in 
the sentence if it is noted between the optional 
operator: << constituent >>. It is a well-known 
fact that we can get optional constituents in DCG 
from making use of a nonterminal symbol (e.g. 
optA, with optA--->A and optA-~\[\]). However this 
skill obliges us to add new nonterminal symbols, 
whereas SUG allows us to get it without adding 
any new one. We can get an example from Figure 
1, in which we can see the reduction of 
grammatical rules in SUG. 
DCG Grammar: 
np -> subst. \[ SUG Grammar: 
np -> det, subst. IlnP ++> <<det>>, <<adj>>, np->det, adj, subst. 
np->det, subst, adj. L subst, <<adj>>, <<pp>>. 
np ->det, subst, pp, 
~CG Grammar with optional constituents: 
np -> optDet, optAdj, subst, optAdj, optPP. 
optDet -> det. optAdj-> adj. ..... 
optDet -> \[\]. optAdj -> \[\]. 
Figure 1. Comparison between DCG and SUG 
with reference to optional constituents. 
Furthermore, this optional operator has the 
possibility of reminding whether the optional 
constituent has been parsed in the sentence or not. 
This information will be very useful in the 
resolution of NLP problems such as ellipsis or 
386 
extraposition. This fact is carried out by adding a 
label to the optional constituent, e.g. << SSNP" 
np >>. This label will be an uninstantiated Prolog 
variable if constituent np is missing, so Prolog 
predicate var (SSNP) would success. 
We have developed a translator which turns 
SUG rules into Prolog clauses. This translator has 
been run under SICStus Prolog 2.1 and Arity 
Prolog 5.1, and it will translate into Prolog each 
SUG production rule. This translator will provide 
what we call slot structure (henceforth SS). 
This SS stores the syntactic, morphologic and 
semantic information of every constituent of the 
grammar. Each SS consists of a structure with 
functor the name of the constituent (np, vp .... ). 
Its first argument corresponds to another structure 
with functor conc which includes all the 
arguments of the constituent (Number, Gender, 
SemanticType). The second one corresponds to 
the 3.p of the final logical formula of the 
constituent. And the remaining arguments 
correspond to the SS of its subconstituents. In this 
SS the parser leaves as uninstantiated Prolog 
variables ("_") the slots corresponding to the 
optional constituents that do not appear in the 
sentence, in this way, we know what has been 
parsed and what has not. From now on we will 
show each SS with 3.p and conc only if it is 
necessary, in order to get simplicity. 
Se,t .ce )_.f 1' .roo, o oc .... to the Dictionary 
I lo, st ctu,,I 
I Processof~solutionofNLPproblems: ~ anaphora, ellipsis, PP-atachraent, ... 
bTnal Slot Structure without these NLP problems \[ 
Figure 2 
Now we would like to make clear the process in 
which we obtain the final logical formula. First of 
all we parse the sentence, and then we get its SS. 
After that, it would be the moment in which we 
could try to solve NLP problems such as 
extraposition, ellipsis, PP-attachment and 
anaphora. The solution will consist of a new SS 
which will be used to obtain the final logical 
formula. This process has been summed up in 
Figure 2. We would like to emphasize that this 
skill of resolution allows us to produce modular 
NLP systems in which grammatical rules, logical 
formulas and the module of resolution of NLP 
problems are quite independent from each other. 
Our SUG parser will access the dictionary only 
once during the whole process of parsing in order 
to avoid repeated access to the same word from 
the dictionary. It stores the information of each 
word on a list before starting the parse and it will 
work with this structure instead of the list of 
words of a DCG parser in Prolog; e.g. DCG list: 
\[this, book, is, mine\], SUG list: \[word (this, \[adj 
(sing, dem), pron (sing, dem)\] ), word (book, 
\[noun (...)\]) .... \]. Each element from the SUG list 
is a structure with name word and with two 
arguments. The first one corresponds to the same 
word of the sentence like a Prolog atom. The 
second one corresponds to a structure list which 
refers to the lexical entries of the word. That is to 
say that every time the parser has to access a 
lexical entry of a word, it will look it up in this 
list; it will not access the dictionary ever again. 
2 Partial parsing with SUG 
In Abney (1997) it is considered necessary to 
carry out a partial parsing on the unrestricted text 
instead of a complete parsing, both due to errors 
and the unavoidable incompleteness of lexicon 
and grammar. It is also difficult to do a global 
search efficiently with unrestricted text, due to 
the length of sentences and the ambiguity of 
grammars. Partial parsing is considered a 
response to these difficulties. Partial parsing 
techniques aim to recover syntactic information 
efficiently and reliably from unrestricted text, by 
sacrificing completeness and depth of analysis. 
In this section we will show the application of 
SUG in partial parsing. We are going to take the 
output of a POS tagger as input, and after apply a 
partial parsing with SUG. The previously 
mentioned corpus The blue book is going to be 
worked on, which has been automatically tagged 
by the Spanish version of the Xerox tagger. Each 
word in a tagged sentence has the following 
syntax: (surfaceForm, lemma, TAG). 
387 
-- i(cormcetions, connection, NCFP) .... : 
(f lnterface in order to~ i\[ ";'o;~i,";,.ib;g~',ii~o'.'\]'): go;,~'(i/'g: " "\] 
\[ map each tag into the \[~:\[art( fem, pl,det)\]), word (¢onnection, : 
\[ aproprlate labehnto\[ :\[noun (common,fem,pl)\]), ... \] : 
the SUG grammar J " 
i 
~ Partial parsing ~ ..................................... with SUG J ~ ISUG grammar in Figure 5 that will 
ionly parse certain constituents 
• Slot Structure that will be 
used in anaphora resolution ) 
Figure 3. Interface between the tagger and SUG. 
We will proceed in the way that is described in 
Figure 3. Firstly the tagged sentence is turned into 
the SUG list format, where each Xerox tag is 
mapped into the apropriate label into the SUG 
grammar, e.g. the Xerox tag (connections, 
connection, NCFP) is mapped into the SUG tag 
word (connection, \[noun (common, fem, pl)\]). 
Finally, this SUG list of words will be taken as 
input for the grammar described in Figure 4. This 
grammar will carry out the partial parsing of the 
text, and the SUG parser will produce the SS that 
will be used in the algorithm, which is proposed 
for anaphor resolution. This simple interface 
between the tagger and SUG is one of the 
advantages of the modularity that presents SUG. 
It will allow us to work with different dictionaries 
or taggers with the same SUG grammar. This is 
due to the fact that in this system there is a great 
independence between the grammar, the lexicon, 
the process of dealing with NLP problems and the 
process of obtaining the final logical formula. 
sentence ++> 
<< PP:pp >>, << NP:np >>, <<P:pronoun>>, 
<< V:verb >>, <<C:conj>>, 
<#\[1, remainingSentence(PP, NP, P, V, C) 
#>. 
remainingSentence(PP, NP, P, V, C) ++> 
<t## ( {(var(PP), var(NP), vat(P), var(V), var(C))}, IVV\]), 
(_ _) ~/f>, 
sentence. 
% . Grammatical rules for each constituent to parse 
coordinated( pp, simplePP ). 
simplePP ++> preposition, np. 
coordinated( np, simpleNP 0 ). 
simpleNP (substantive Type) ++> <<determiner>>, 
<<adjective>>, noun, <<pp>>. 
simpleNP (adjective Type) ++> <<determiner>>, adjective, 
<<pp>>. 
Figure 4. Partial parsing with SUG. 
The grammar in Figure 4 will only parse 
coordinated prepositional phrases (pp), 
coordinated noun phrases (np), pronouns (p), 
conjunctions (conj) and verbs (verb) in whatever 
order that they appear in the text and it will allow 
us to work in a similar way that the algorithm 
mentioned in Kennedy and Boguraev (1996). But 
in our approach we will automatically get the 
syntactic information from this grammar. The SS 
returned by the parser will consist of a sequence 
of these constituents: pp, np, p, conj, verb and 
free words. The attachments (e.g. of the pp) will 
be postponed to the module of resolution of NLP 
problems, which could work jointly with the 
algorithm for anaphor resolution (in a similar way 
to the approach proposed in Azzam (1995)). The 
free words will consist of constituents that are not 
covered by the grammar (e.g. adverbs) or words 
that are not important for the anaphor resolution. 
The output of the whole system will consist of a 
sequence of the logical formulas of each 
constituent. 
Here sentence will be the initial symbol of the 
grammar and the partial parsing will be applied 
with the rules shown in Figure 4. If we want a 
complete parsing, we just have to substitute these 
rules for the following: sentence ++> np, vp, and 
obviously we will have to add the grammatical 
rule for a verbal phrase (vp). 
3 The algorithm 
In this section we are going to propose an 
algorithm which can deal with discourse 
anaphora in unrestricted texts with partial 
parsing. It is based on the process of parsing 
described in Figure 3. So this process will take 
the output of a POS tagging as input, and it will 
be applied after the partial parsing of a sentence 
(using the grammar described in Figure 4) and 
before obtaining its logical formula. 
This algorithm is shown in Figure 5 and it will 
deal with pronominal references, surface-count 
anaphora and one-anaphora. This algorithm will 
take a slot structure (SS) that consists of a 
sequence of the following constituents: np, pp, p, 
conj and verbs and it will return a new one 
without anaphors. Every possible antecedent 
(noun phrases) will be stored in a list of 
388 
antecedents, that will be used to solve the 
anaphors. Another structure will be stored in this 
list for each antecedent: paral (Sent, Clause, 
PosVerb, NumConst, NumCoord). This structure 
will be used to deduce the parallelism with partial 
parsing between an anaphor and its antecedent. 
Its first argument, Sent, is the sentence in which 
the antecedent appears. The second one is the 
clause in which it appears. Consider that the 
beginning of a new clause has been found when 
we parse a free conjuction (we do not refer to the 
conjunctions that join the coordinated noun and 
prepositional phrases). The third one is the 
position of the antecedent with reference to the 
verb of the clause: before (bv) or after (av). The 
fourth one is the number of constituent in the 
sentence and the fifth one is the number of 
coordinated constituent if it is included in a 
coordinated np or pp. For example in: He said 
that Peter and John bought a book, we have the 
following: paralm (S, 1, bv, 1, 1), paraljoh, (S, 2, 
bv, 4, 2) and paralbook (S,2,av,6,1). 
Parse a sentence. We obtain its slot structure (SS1). 
For each anaphor in SSI: 
Select the antecedents of the previous X sentences 
depending on the kind of anaphor in LO 
Apply constraints (depending on the kind of anaphor) to LO 
with a result of LI: 
Case of: 
IL ll = I Then: 
This one will be the antecedent of the anaphor 
ILII • 1 Then: 
Apply preferences (depending on the kind of enaphor) to 
L 1, with a result of L2: 
The first one of L2 will be the selected antecedent 
Update SSf with each antecedent of each anaphor with a 
result of SS2. 
Figure 5. Algorithm for anaphor resolution. 
At the same time that we are searching for 
antecedents, we will also search for anaphors and 
whenever we found an anaphor this algorithm 
will be applied. The kind of anaphors we are 
going to search are the following: pronouns (he, 
she .... ), pronominal noun phrases formed by: 
determiner + pronoun (the second, the former, 
...), noun phrases with the structure: determiner + 
adjective + "one" (the red one, this anaphors in 
Spanish 3 are noun phrases in which the noun has 
3 We are going to work with Spanish unrestricted 
texts, but whenever it is possible, all the examples will 
be translated into English in order to facilitate its 
understanding. 
been omitted: el rojo). We will identify such 
anaphors from its SS (its functor and its number 
and type of arguments). For example, the one- 
anaphor in Spanish will have the following SUG 
rule: np ++> <<determiner>>, adjective, 
<<pp>>, and the following SS: np (determiner 
(...), adjective (...), pp (...)). 
The number of previous sentences considered 
in the resolution of an anaphor will be determined 
by the kind of anaphor itself. For pronominal 
references will be considered the antecedents in 
the same sentence or in the previous sentence if it 
is in the same paragraph, unlike to one-anaphora 
which have more lexical information, so we will 
consider the antecedents in the same paragraph. 
We will be able to know the number of sentence 
because this information will be stored jointly 
with the SS of every antecedent: for each 
sentence will be assigned a different Prolog 
variable and all the antecedents in this sentence 
will have this variable in itsparal structure. 
The algorithm will apply a set of constraints to 
the list of possible antecedents in order to 
discount candidates. If there is only one 
candidate, this one will be the antecedent of the 
anaphor. Otherwise, if there are still more than 
one candidates left, a set of preferences will be 
applied that will sort the list of remaining 
antecedents, and the selected antecedent will be 
the first one. It is important to remark that these 
constraints and preferences could be different for 
each kind of anaphor. 
Next the constraints and preferences are going 
to be briefly explained. Morphosyntactic 
agreement (person, gender and number) will be 
checked by unification of the structure conc 
described in section 1. It is a strong constraint on 
reference, but it is not absolute: At the zoo, a 
monkey scampered between two elephants. One 
snorted at it 4, or in: John and Bill~ went into the 
shop. They~ bought a book. To solve the second 
example we will store a new antecedent with 
plural number which includes all the coordinated 
noun phrases (in this case John and Bill). We will 
detect the coordination of noun phrases from the 
SS returned by the SUG fact coordinated. In one- 
4 In this paper we will not deal with problems 
caused by quantification. 
389 
anaphora we have considered the number 
agreement as a preference instead of a constraint 
in order to solve sentences like this: Wendy didn't 
give either boy a green shirti, but she gave Sue 
two red onesj, where the anaphor and its 
antecedent do not agree in number (so they do not 
co-refer to the same entity of the discourse). 
The c-command constraints will be applied on 
the syntactic information stored in the SS of each 
constituent and its structure paral. For example 
the following constraint: "A pronominal NP must 
be interpreted as non-coreferential with any NP 
that c-commands it", e.g. Zeldai bores herj. It is 
accomplished by the information stored in their 
structures: paral~ (Sent1, Clause1 .... ) and paralj 
(Sent1, Clause1 .... ) which means that they are in 
the same sentence and clause. However in Johnj 
was late for work, because he~ slept in, here John 
and he can be coreferential because they are in 
different clauses separated by the conjunction 
because: paraljoh, (Senti, Clausel .... ), paralh~ 
(Sent1, Clause2 .... ). But in John~ and hej bought 
a book, the pronoun will not corefer with John 
although there is a conjunction between them 
because they are in the same coordinated noun 
phrase, which is known from: parali ($1, C1, by, 
1_, 1) and paralj ($1, C1, by, 1, 2). In sentences 
like (John~ 's portrait of himj)ue is interesting and 
This is (the mani who hej saW)N P the coreference is 
not permitted because the pronoun and the 
antecedent are in the same constituent NP (they 
are in the same slot structure: np (det (the), noun 
(man), relSent (...)). As well in John bought a 
book for Peteri and for a friend of him~, the 
pronoun can corefer with Peter although they 
belong to the same coordinated constituent 
because the pronoun is an adjunct of the second 
coordinated constituent. From the reflexivity 
constraints in Maryj loves herse~, we can 
conclude the antecedent of herself is Mary 
because they are in the same clause. 
In relation to preferences, they will be different 
for each kind of anaphor: the non-reflexive 
pronouns will prefer the antecedent in the same 
sentence and clause, and if there are still more 
than one antecedent left, those in the same 
position with reference to the verb: syntactic 
parallelism. Moreover we have added some other 
preferences, e.g. a non-reflexive pronoun would 
not be allowed to have an antecedent that appear 
in the same clause due to reflexivity constraints: 
Jacki saw Samj at the party. Samj gave himi a 
drink. If after applying these preferences, there 
are more than one antecedent left, we will choose 
the antecedent most recently mentioned. 
In order to solve surface-count anaphora we 
will use the SS returned by the SUG fact 
coordinated. This fact allows the coordination of 
constituents with the same or different form: 
Peter, your daughter and she and it will allow us 
to access whatever coordinated constituent in the 
order we wish. That is to say, its SS: np 
(simpleNP (Peter), conj(', '), np (simpleNP (det 
(your), noun (daughter)), conj (and), np 
(simpleNP (pron (she)), , _))), and their 
structures paral with their fifth argument will tell 
us the number of coordinated constituent: 
paralp,,er (S, C, V, P, 1), paraldaugh,e, (S, C, V, P, 2), 
.... In this way the anaphor: the second one will 
choose an antecedent with a structure paral with 
a value of 2 in its fifth argument. 
To solve one-anaphora we will apply the 
following preference: we will choose the 
antecedents with a similar structure. For example, 
in Wendy didn't give either boy a green tie-dyed 
T-shirti, but she gave Sue a blue onej, the 
antecedent a green tie-dyed T-shirt would be 
chosen instead of Wendy or Sue because they 
have similar SS (a determiner, a common noun 
and an adjective): np (noun(Wendy)), npi (~, det 
(a), adj (\[green, tie-dyed\]5), noun (T-shirt)) and 
npj ~, det (a), adj (\[blue\]), pron (one)). This SS 
will allow decomposition of the description (i.e. 
green can be broken off) and the solution of the 
anaphora will be: np (Y, det (a), adj (\[blue\]), 
noun (T-shirt)). It is important to remark that the 
solution will have a different variable 6 (Y) than its 
antecedent (X). It means the anaphor and its 
antecedent do not co-refer, so the anaphor refers 
to a new entity in the discourse. However in John 
bought a red dark apple~ and a green pear. He ate 
the red one~, the anaphor will co-refer with a red 
dark apple. We will distinguish both cases 
s This list of adjectives is provided by the SUG fact 
juxtaposition. 
6 This variable corresponds to the ~.p of the final 
logical formula of the constituent (see section 1). 
390 
because in the second one the anaphor and its 
antecedent share the same modifiers 7 (red) and 
they agree in number. 
4 Evaluation of the system 
We have run our system on part of the previously 
mentioned corpus (9600 words), and we have got 
the following figures. Our system has detected 
100% of the anaphors described in this paper, and 
the partial parsing described in Figure 4, has 
parsed 81% of words with a very simple 
grammar 8. The medium length of the sentences 
with anaphors is 48 words. For pronominal 
references we have a 83% accuracy in detecting 
the position of the antecedent. For one-anaphora 
and surface-count anaphora, we have not got 
significant figures since there were not so many 
anaphors as we wished (only 5 anaphors with a 
80% accuracy). The reason why some of the 
references have failed is mainly due to the lack of 
semantic information and due to the problem of 
attachments between different parsed 
constituents 9. 
Conclusions 
In this paper we have proposed a computational 
approach to the resolution of pronominal 
references, surface-count anaphora and one- 
anaphora. This approach works on the output of a 
POS tagger, on which we will automatically 
apply a partial parsing from the formalism: Slot 
Unification Grammar. We have only used lexical, 
morphologic and syntactic information. We have 
slightly '° improved the accuracy (83%) in 
pronominal references to the work of Kennedy 
and Boguraev (1996) (75%), but we have also 
improved that approach since we automatically 
7 It is obvious that we will probably need more 
semantic information in order to solve these anaphors, 
but in this paper we are not going to consider this 
information since the tagger does not provide it. 
s We could easily improve this percentage from 
adding more constituents to the grammar (e.g. adverbs 
or punctuation marks). 
9 To solve this problem is also necessary semantic 
information. 
,o It is difficult to compare both measures because 
we have worked on different texts (Spanish texts). 
apply a partial parsing and we deal with other 
kinds of anaphors. 
As a future aim we will include semantic 
information in our algorithm in order to check the 
improvement that we get with it. This information 
will be stored in a dictionary which could be 
automatically consulted (since this semantic 
information is not provided by the tagger). 

References 
Abney S. (1997) Part*of-Speech Tagging and Partial 
Parsing. In Steve Young and Gerrit Bloothooft (eds) 
Corpus-based methods in language and speech 
processing. Kluwer Academic Publishers 
Azzam S. (1995) An Algorithm to Co-Ordinate 
Anaphor resolution and PPS Disambiguation 
Process. EACL 
Baldwin B. (1997) CogNIAC: high precision 
coreference with limited knowledge and linguistic 
resources. ACL/EACL workshop on Operational 
factors in practical, robust anaphor resolution 
Connoly D., Burger J. and Day D. (1994) A Machine 
learning approach to anaphoric reference. 
International Conference on New Methods in 
Language Processing, UMIST 
Ferdmdez A., Palomar M. and Moreno L. (1997a) Slot 
Unification Grammar. Joint Conference on 
Declarative Programming. APPIA-GULP-PRODE 
Ferr6ndez A., Palomar M. and Moreno L. (1997b) Slot 
Unificacion Grammar and anaphor resolution. 
Recent Advances in Natural Language Processing 
Kennedy C. and Boguraev B. (1996) Anaphora for 
Everyone: Pronominal Anaphor resolution without a 
Parser. COLING 
Lappin S. and Leass H. (1994) An algorithm for 
pronominal anaphor resolution. Computational 
Linguistics, 20(4) 
Mitkov R. (1997) Pronoun resolution: the practical 
alternative". In S. Botley, T. McEnery (eds) 
Discourse Anaphora and Anaphor Resolution, Univ. 
College London Press 
Mitkov R. (1995) An uncertainty reasoning approach 
to anaphor resolution. Natural Language Pacific Rim 
Symposium. Seoul. Korea 
Mitkov R. and Stys M. (1997) Robust reference 
resolution with limited knowledge: high precision 
genre-specific approach for English and Polish. 
Recent Advances in Natural Language Processing 
