Robust Parsing Based on Discourse Information: 
Completing partial parses of ill-formed sentences 
on the basis of discourse information 
Tetsuya Nasukawa 
IBM Research, Tokyo Research Laboratory 
1623-14, Shimotsurmna, Yamato-shi, Kanagawa-ken 242, Japan 
nasukawaOtrl, vnet. ibm. com 
Abstract 
In a consistent text, many words and 
phrases are repeatedly used in more than 
one sentence. When an identical phrase 
(a set of consecutive words) is repeated in 
different sentences, the constituent words 
of those sentences tend to be associated in 
identical modification patterns with identi- 
cal parts of speech and identical modifiee- 
modifier relationships. Thus, when a 
syntactic parser cannot parse a sentence 
as a unified structure, parts of speech 
and modifiee-modifier relationships among 
morphologically identical words in com- 
plete parses of other sentences within the 
same text provide useful information for 
obtaining partial parses of the sentence. 
In this paper, we describe a method for 
completing partial parses by maintaining 
consistency among morphologically identi- 
cal words within the same text as regards 
their part of speech and their modifiee- 
modifier relationship. The experimental 
results obtained by using this method with 
technical documents offer good prospects 
for improving the accuracy of sentence 
analysis in a broad-coverage natural lan- 
guage processing system such as a machine 
translation system. 
1 Introduction 
In order to develop a practical natural language pro- 
cessing (NLP) system, it is essential to deal with 
ill-formed sentences that cannot be parsed correctly 
according to the grammar rules in the system. In 
this paper, an "ill-formed sentence" means one that 
cannot be parsed as a unified structure. A syntac- 
tic parser with general grammar rules is often un- 
able to analyze not only sentences with grammati- 
cal errors and ellipses, but also long sentences, ow- 
ing to their complexity. Thus, ill-formed sentences 
include not only ungrammatical sentences, but also 
some grammatical sentences that cannot be parsed 
as unified structures owing to the presence of un- 
known words or to a lack of completeness in the 
syntactic parser. In texts from a restricted domain, 
such as computer manuals, most sentences are gram- 
matically correct. However, even a well-established 
syntactic parser usually fails to generate a unified 
parsed structure for about 10 to 20 percent of all the 
sentences in such texts, and the failure to generate 
a unified parsed structure in syntactic analysis leads 
to a failure in the output of a NLP system. Thus, 
it is indispensable to establish a correct analysis for 
such a sentence. 
To handle such sentences, most previous ap- 
proaches apply various heuristic rules (Jensen et 
al., 1992; Douglas and Dale, 1992; Richardson and 
Braden-Harder, 1988), including 
• Relaxing constraints in the condition part of a 
grammatical rule, such as number and gender 
constraints 
• Joining partial parses by using meta rules. 
Either way, the output reflects the general plausibil- 
ity of an analysis that can be obtained from infor- 
mation in the sentence; however, the interpretation 
of a sentence depends on its discourse, and incon- 
sistency with recovered parses that contain different 
analyses of the same phrase in other sentences in the 
discourse often results in odd outputs of the natural 
language processing system. 
Starting from the viewpoint that an interpretation 
of a sentence must be consistent in its discourse, we 
worked on completing incomplete parses by using 
information extracted from complete parses in the 
discourse. The results were encouraging. Since most 
words in a sentence are repeatedly used in other sen- 
tences in the discourse, the complete parses of well- 
formed sentences usually provided some useful infor- 
mation for completing incomplete parses in the same 
discourse. Thus, rather than trying to enhance a 
syntactic parser's grammar rules in order to support 
ill-formed sentences, which seems to be an endless 
task after the parser has obtained enough coverage 
to parse general grammatical sentences, we treat the 
39 
syntactic parser as a black box and complete incom- 
plete parses, in the form of partially parsed chunks 
that a bottom-up parser outputs for ill-formed sen- 
tences, by using information extracted from the dis- 
course. 
In the next section, the effectiveness of using in- 
formation extracted from the discourse to complete 
syntactic analysis of ill-formed sentences. After that, 
we propose an algorithm for completing incomplete 
parses by using discourse information, and give the 
results of an experiment on completing incomplete 
parses in technical documents. 
2 Discourse information for 
completing incomplete parses 
In this section, we use the word "discourse" to 
denote a set of sentences that forms a text con- 
cerning related topics. Gale (Gale et al., 1992) and 
Nasukawa (Nasukawa, 1993) reported that polyse- 
mous words within the same discourse have the same 
word sense with a high probability (98% accord- 
ing to (Gale et al., 1992),) and the results of our 
analysis indicate that most content words are fre- 
quently repeated in the discourse, as is shown in 
Table 1; moreover, collocation (modifier-modifiee re- 
lationship) patterns are also repeated frequently in 
the same discourse, as is shown in Figure 1. This 
figure reflects the analysis of structurally ambiguous 
phrases in a computer manual consisting of 791 con- 
secutive sentences for discourse sizes ranging from 
10 to 791 sentences. For each structurally ambigu- 
ous phrase, more than one candidate collocation pat- 
tern was formed by associating the structurally am- 
biguous phrase with its candidate modifiees 1 and a 
collocation pattern identical with or similar to each 
of these candidate collocation patterns was searched 
for in the discourse. An identical collocation pattern 
is one in which both modifiee and modifier sides con- 
sist of words that are morphologically identical with 
those in the sentence being analyzed, and that stand 
in an identical relationship. A similar collocation 
pattern is one in which either the modifiee or modi- 
tier side has a word that is morphologically identical 
with the corresponding word in the sentence being 
analyzed, while the other has a synonym. Again, 
the relationship of the two sides is identical with 
that in the sentence being analyzed. Except in the 
case where all 791 sentences were referred to as a 
discourse, the results indicate the averages obtained 
by referring to each of several sample areas as a dis- 
course. For example, to obtain data for the case in 
which the size of a discourse was 20 sentences, we 
examined 32 areas each consisting of 20 sentences, 
1 For example, in the sentence 
You can use the folder on the desktop, 
the ambiguous phrase, on the desktop, forms two candi- 
date collocation patterns: 
"use -(on)- desktop" and '%lder -(on)- desktop." 
such as the 1st sentence to the 20th, the 51st to the 
70th, and the 701st to the 720th. Thus, Figure 1 
indicates that a collocation pattern either identical 
with or similar to at least one of the candidate collo- 
cation patterns of a structurally ambiguous phrase 
was found within the discourse in more than 70% of 
cases, provided the discourse contained more than 
300 consecutive sentences. 
On the assumption that this feature of words in a 
discourse provides a clue to improving the accuracy 
of sentence analysis, we conducted an experiment 
on sentences for which a syntactic parser generated 
more than one parse tree, owing to the presence of 
words that can be assigned to more than one part 
of speech, or to the presence of complicated coor- 
dinate structures, or for various other reasons. If 
the constituent words tend to be associated in iden- 
tical modification patterns with an identical part 
of speech and identical modifiee-modifier relation- 
ship when an identical phrase (a set of consecutive 
words) is repeated in different sentences within the 
discourse, the candidate parse that shares the most 
collocation patterns with other sentences in the dis- 
course should be selected as the correct analysis. 
Out of 736 consecutive sentences in a computer man- 
ual, the ESG parser (McCord, 1991) generated mul- 
tiple parses for 150 sentences. In this experiment, we 
divided the original 736 sentences into two texts, one 
a discourse of 400 sentences and the other a discourse 
of 336 sentences. Of the 150 sentences with multiple 
parses, 24 were incorrectly analyzed in all candidate 
parses or had identical candidate parses; we there- 
fore focused on the other 126 sentences. In each 
candidate parse of these sentences, we assigned a 
score for each collocation that was repeated in other 
sentences in the discourse (in the form of either an 
identical collocation or a similar collocation), and 
added up the collocation scores to assign a prefer- 
ence value to the candidate parse. Out of the 126 
sentences, different preference values were assigned 
to candidate parses in 54 sentences, and the highest 
value was assigned to a correct parse in 48 (88.9%) 
of the 54 sentences. Thus, there is a strong tendency 
for identical collocations to be actually repeated in 
the discourse, and when an identical phrase (a set 
of consecutive words) is repeated in different sen- 
tences, their constituent words tend to be associated 
in identical modification patterns. 
Figure 2 shows the output of the PEG parser 
(Jensen, 1992) for the following sentence: 
(2.1) As you can see, you can choose from many 
topics to find out what information is available 
about the AS/400 system. 
This is the 53rd sentence in Chapter 6 of a computer 
manual (IBM, 1992), mid every word of it is repeat- 
edly used in other sentences in the same chapter, as 
shown in Table 2. For example, the 39th sentence 
in the same chapter contains "As you can see," as 
40 
Table 1: Frequency of morphologically identical words in computer manuaJs 
Part Freq. of morph, identical words Proportion of all content words 
of Two or more Five or more Total number of Proportion 
speech times (%) times (%) appearances (words) (%) 
Noun 90.7 76.2 99047 59.8 
Verb 94.9 83.6 35622 21.5 
Adjective 88.9 71.0 16941 10.2 
Adverb 68.8 4993 3.0 
Pronoun 
85.9 
98.0 94.8 8911 5.4 
Total \[ 91.6 78.0 165514 I -- 
Rate of repetition (%) 
100.00 -- 
80.00 -- 
60.00- 
40.00 - 
20.00- 
0.00- 
J 
0 200 400 600 
Size of discourse 
800 (Number of sentences) 
Figure 1: Rate of finding identical or similar collocation patterns in relation to the size of the discourse 
shown in Figure 3. The sentences that contain some 
words in common with sentence (2.1) provide infor- 
mation that is very useful for deriving a correct parse 
of the sentence. Table 2 also shows that the parts 
of speech (POS) for most words in sentence (2.1) 
can be derived from words repeated in other sen- 
tences in the same chapter. In this table, the up- 
percase letters below the top sentence indicate the 
parts of speech that can be assigned to the words 
above. Underneath the candidate part of speech, re- 
peated phases in other sentences are presented along 
with the part of speech of each word in those sen- 
tences; thus, the first word of sentence (2.1), "As," 
can be a conjunction, an adverb, or a preposition, 
but complete parses of the 39th and 175th sentences 
indicate that in this discourse the word is used as a 
conjunction when it is used in the phrase "As you 
ca~ see." 
Furthermore, information on the dependencies 
among most words in sentence (2.1) can be extracted 
from phrases repeated in other sentences in the same 
chapter, as shown in Figure 4. ~ 
2Thick arrows indicate dependencies extracted fl'om 
the discourse information. 
3 Implementation 
3.1 Algorithm 
As we showed in the previous section, information 
that is very useful for obtaining correct parses of ill- 
formed sentences is provided by complete parses of 
other sentences in the same discourse in cases where 
a parser cannot construct a parse tree by using its 
grammar rules. In this section, we describe an al- 
gorithm for completing incomplete parses by using 
this information. 
The first step of the procedure is to extract fi'om 
an input text discourse information that the system 
can refer to in the next step in order to complete in- 
complete parses. The procedure for extracting dis- 
course information is as follows: 
1. Each sentence in the whole text given as a dis- 
course is processed by a syntactic parser. Then, 
except for sentences with incomplete parses and 
multiple parses, the results of each parse are 
stored as discourse information. To be pre- 
cise, the position and the part of speech of 
each instance of every lemma are stored along 
with the lemma's modifiee-modifier relation- 
ships with other content words extracted from 
41 
((XXXX (COMMENT(CONJ 
(NP 
(AUXP 
(VERB* 
(PUNC ",") 
(VP (NP 
(AUXP 
(VERB* 
(PP 
(VP* (INFCL 
(NP 
(VERB* 
(AJP ? 
(PUNC ". ") ) 
"as") 
(PRON* "you" ("you" (SG PL)))) 
(VERB* "can" ("can" PS))) 
"see" ("see" PS))) 
(PRON* "you" ("you" (SG PL)))) (VERB* "can" ("can" PS))) 
"choose" ("choose" PS)) 
(PP (PREP* "from")) 
(QUANP (ADJ* "many" ("many" BS))) 
(NOUN* "topics" ("topic" PL)))) 
(INFT0 (PREP* "to") ) 
(VERB* "find" ("find" PS)) 
(COMPCL (COMPL "") 
(VERB* "out" ("out" PS)) 
(NP (PRON* "vhat" ("what" (SG PL)))))) 
(NOUN* "information" ("information" SG))) 
"is" ("be" PS)) 
(ADJ* "available" ("available" BS)) 
(PP (PP (PREP* "about") ) 
(DETP (ADJ* "the" ("the" BS))) 
(NP (NOUN* "AS/400" ("AS/400" (SG PL)))) (NOUN* "system" 
("system" SG))))) 
0) 
Figure 2: Example of an incomplete parse obtained by the PEG parser 
As you can see, the help display provides additional information about the menu options 
ava/lable, as well as a list of related topics. 
((DECL (SUBCL 
(NP 
(VERB* 
(CONJ "as") 
(NP (PRON* "you" ("you" (SG PL)))) 
(AUXP (VERB* "can" ("can" PS))) 
(VERB* "see" ("see" PS)) 
(PUNC ,,,,,)) 
(DETP (ADJ* "the" ("the" BS))) 
(NP (NOUN* "help" ("help" SG))) 
(NOUN* "display" ("display" SG))) 
"provides" ("provide" PS)) 
Figure 3: Thirty-ninth sentence of Chapter 6 and a part of its parse 
the parse data. Table 3 shows an example of 
such information. In this table, CFRAMEuuuuuu 
indicates an instance of cursor in the discourse; 
information on the position and on the whole 
sentence can be extracted from each occurrence 
of CFRAME. In accumulating discourse informa- 
tion, a score of 1.0 is awarded for each definite 
modifiee-modifier relationship. A lower score, 
0.1, is awarded for each ambiguous modifiee- 
modifier relationship, since such relationships 
are less reliable. 
2. When all the sentences have been parsed, the 
discourse information is used to select the most 
preferable candidate for sentences with multi- 
ple possible parses, and the data of the selected 
parse are added to the discourse information. 
After all the sentences except the ill-formed sen- 
tences that caused incomplete parses have provided 
data for use as discourse information, the parse com- 
pletion procedure begins. 
The initial data used in the completion procedure 
are a set of partial parses generated by a bottom-up 
parser as an incomplete parse tree. For example, the 
PEG parser generated three partial parses for sen- 
tence (2.1), consisting of "As you can see," "you can 
choose from many topics," and "to find out what 
information is available about the AS/400 system," 
as shown in Figure 2. Since partial parses are gen- 
erated by means of grammar rules in a parser, we 
decided to restructure each partial parse and unify 
them according to the discourse information, rather 
than construct the whole parse tree from discourse 
information. 
The completion procedure consists of two steps: 
Step 1: Inspecting each partial parse and 
restructuring it on the basis of the discourse 
information 
For each word in a partial parse, the part of speech 
and the rood,flee-modifier relationships with other 
words are inspected. If they are different from those 
42 
Table 2: Selecting POS candidates on the basis of discourse information 
As you can see, you can choose from many topics to find out 
Candidates CJ PN N N PN N V PP AJ N PP N PP 
for the POS AV V V V N V N 
of each word PP PN AV PP 
V 
As you can see, appears in sentences 39, 175. 
Phrases 
repeated 
within the 
discourse 
CJ PN V V you can choose appears in sentences 179. 
PN V V many appears in sentences 49. 
AJ I topics find out what 
appears in sentences 39, 140 , 145 , 160, 161 167 169... N to find \[ 
appears in sentences 236. PP V 1 
appears in sentences 32. V PP (PN) 
POS CJ PN V V PN V V PP AJ N PP V PP 
what information is available about the AS/400 system. 
Candidates AJ N V AJ AJ DET N N 
for the POS AV AV 
of each word PN PP 
Phrases what information is available about the appears in sentences 49. 
repeated AJ N V AJ PP DET 
within the the AS/400 system. 
discourse appears in sentences 6, 109, 115. DET N N 
POS PN N V AJ PP DET N N 
AJ 
N=noun PN= ~ronoun V=verb A J----adjective AV=adverb CJ=conjunction PP=preposition DET=determiner 
".°, 
Figure 4: Constructing a dependency structure by 
combining dependencies existing within phrases that 
occur in other sentences of the same chapter 
in the discourse information, the partial parse is re- 
structured according to the discourse information. 
For example, Figure 5 shows an incomplete parse 
of the following sentence, which is the 43rd sentence 
in a technical text that consists of 175 sentences. 3 
(3.1) Fig. 3 is an isometric view of the magazine 
taken from the operator's side with one car- 
tridge shown in an unprocessed position and 
two cartridges shown in a processed position. 
In the second partial parse, the word "side" is an- 
alyzed as a verb. The same word appears fifteen 
times in the discourse information extracted from 
well-formed sentences, and is analyzed as a noun ev- 
ery time it appears in complete parses; furthermore, 
there are no data on the noun "operator" modify- 
ing the verb "take" through the preposition "from," 
while there is information on the noun "operator's" 
modifying the noun "side," as in sentence (3.2), and 
on the noun "side" modifying the verb "take," as in 
sentence (3.3). 
(3.2) In the operation of the invention, an oper- 
ator loads cartridges into the magazine from 
3This structure resulting from an incomplete parse 
does not indicate that the grammar of the parser lacks a 
rule for handling a possessive case indicated by an apos- 
trophe and an s. When the parser fails to generate a 
unified parse, it outputs partial parses in such a manner 
that fewer partial parses cover every word in the input 
sentence. 
43 
Table 3: Discourse information on modifiees and modifiers of a noun "cursor" 
Modifiers 
POS Relation Word (CFRAMEs preference value) 
Noun of display (CFRAME106873 0.1) 
in protected area (CFRAME106872 1) 
to left (CFRAME106407 0.1) right(CFRAME106338 0.1) 
DIRECT position (CFRAME106405 1) 
Adjective up line (CFRAME106295 0.1) 
DIRECT your (CFRAMEI06690 CFRAMEI06550 2) 
POS Relation 
Verb with 
up 
SUBJ 
OBJ 
RECIPIENT 
Modifiees 
Word (CFRAMEs preference value) 
play (CFRAME106928 0.1) be (CFRAMEI06927 0.1) 
move (CFRAME106688 1) 
stop (CFRAME106572 1) reach (CFRAME106346 1) move (CFRAME106248 1) 
move (CFRAME106402 CFKAME106335 CFRAME106292 3) confuse (CFRAME106548 1) 
move (CFRAME106304 1) 
isometric view (n) I 
~"~f.':~,~ magazine (n) l 
taken Ivll 
~:: ~o.:~o':q operator (n) \] 
. ......... and (conj) \] 
q one cartridge (n) J 
~\[" shown (v) l 
~':!n'~q unprocessed position (n) \] 
two cartridges (n) I 
J shown (v) l 
~,':!ni-- \[ processed position (n) \] 
Figure 5: Example of an incomplete parse by the 
ESG parser 
the operator's side as seen in Figs. 3 and 12. 
(151st sentence) 
(3.3) Fig. 4 is an isometric view of the magazine 
taken from the machine side with one cartridge 
shown in the unprocessed position and two car- 
tridges shown in the processed position. (44th 
sentence) 
Therefore, these two partial parses are restructured 
by changing the part of speech of the word "side" 
to noun, and the modifiee of the noun "operator" to 
otric view (n)J 
~.~ :~'f.':~.~ magazine (n)l 
i from ! 
"~ operator (n)\] 
..! ...... with 
\[ and (conj) \] I 
one cartridge (n)\]   ho.n,v,J 
 -4:.u -Z-oce,sed, pos,,onCn)\] 
two cartridges (n) J 
# ~,~ 
shown (v)J 
~:!n:}--\[ processed position (n) \] 
Figure 6: Example of a completed parse 
the noun "side," while at the same time changing 
the modifiee of the noun "side" to the verb "take." 
As a result, a unifed parse is obtained, as shown in 
Figure 6. 
Step 2: Joining partial parses on the basis of 
the discourse information 
If the partial parses are not unified into a single 
structure in the previous step, they are joined to- 
gether on the basis of the discourse information until 
a unified parse is obtained. 
44 
Partial parses are joined as follows: 
First, the possibility of joining the first two partiM 
parses is examined, then, either the unification of 
the first two parses or the second parse is examined 
to determine whether it can be joined to the third 
parse, then the examination moves to the next parse, 
and so on. 
Two partial parses are joined if the root (head 
node) of either parse tree can modify a node in 
the other parse without crossing the modification of 
other nodes. 
To examine the possibility of modification, dis- 
course information is applied at three different lev- 
els. First, for a candidate modifier and modifiee, 
an identical pattern containing the modifier word 
and the modifiee word in the same part of speech 
and in the same relationship is searched for in the 
discourse information. Next, if there is no identi- 
cal pattern, a modification pattern with a synonym 
(Collins, 1984) of the node on one side is searched 
for in the discourse information. Then, if this also 
fails, a modification pattern containing a word that 
has the same part of speech as the word on one side 
of the node is searched for. 
Since the discourse information consists of mod- 
ification patterns extracted from complete parses, 
it reflects the grammar rules of the parser, and a 
matching pattern with a part of speech rather than 
an actual word on one side can be regarded as a 
relaxation rule, in the sense that syntactic and se- 
mantic constraints are less restrictive than the cor- 
responding grammar rule in the parser. 
These matching conditions at different levels are 
applied in such a manner that partial parses are 
joined through the most preferable nodes. 
3.2 Results 
We have implemented this method on an English-to- 
Japanese machine translation system called Shalt2 
(Takeda et al., 1992), and conducted experiments 
to evaluate the effectiveness of this method. Ta- 
ble 4 gives the result of our experiments on two 
technical documents of different kinds, one a patent 
document (text 1), and the other a computer man- 
ual (text 2). Since text 1 contained longer and 
more complex sentences thml text 2, our ESG parser 
failed to generate unified parses more often in text 
1; on the other hand, the frequency of morpholog- 
ically identical words and collocation patterns was 
higher in text 1, and our method was more effec- 
tive in text 1. In both texts, the discourse infor- 
mation provided enough information to unify par- 
tial parses of an incomplete parse in more than half 
of the cases. However, the resulting unified parses 
were not always correct. Since sentences with in- 
complete parses are usually quite long and contain 
complicated structures, it is hard to obtain a per- 
fect analysis for those sentences. Thus, in order to 
evaluate the improvement in the output translation 
rather than the improvement in the rate of success 
in syntactic analysis, in which only perfect analy- 
ses are counted, we compared output translations 
generated with and without the application of our 
method. When our method was not applied, partial 
parses of an incomplete parse were joined by means 
of some heuristic rules such as the one that joins a 
partial parse with "NP" ill its root node to a partial 
parse with "VP" in its root node, and the root node 
of the second partial parse was joined to the last 
node of the first partial parse by default. When the 
discourse information did not provide enough infor- 
mation to unify partial parses with the application 
of our method, the heuristic rules were applied. In 
such cases the default rule of joining the root node of 
the second partial parse to the last node of the first 
partial parse was mostly applied, since the least re- 
strictive matching patterns in our method were sim- 
ilar to the heuristic rules. Thus, the system gen- 
erated a unified parse for each sentence regardless 
of the discourse information, and we compared the 
output translations generated with and without the 
application of our method. The results are shown in 
Table 4. The translations were compared by check- 
ing how well the output Japanese sentence conveyed 
the meaning of the input English sentence. Since 
most unified parses contained various errors, such as 
incorrect modification patterns and incorrect parts 
of speech assigned to some words, fewer errors gen- 
erally resulted in better translations, but incorrect 
parts of speech resulted in worse translations. 
4 Conclusion 
We have proposed a method for completing partial 
parses of ill-formed sentences on the basis of informa- 
tion extracted from complete parses of well-formed 
sentences in the discourse. Our approach to han- 
dling ill-formed sentences is fundamentally different 
from previous ones in that it reanalyzes the part of 
speech and modifiee-modifier relationships of each 
word in an ill-formed sentence by using information 
extracted from analyses of other sentences in the 
same text, thus, attempting to generate the analy- 
sis most appropriate to the discourse. The results 
of our experiments show the effectiveness of this 
method; moreover, implementation of this method 
on a machine translation system improved the accu- 
racy of its translation. Since this method has a sim- 
ple framework that does not require any extra knowl- 
edge resources or inference mechanisms, it is robust 
and suitable for a practical natural language pro- 
cessing system. Furthermore, in terms of the turn- 
around time (TAT) of the whole translation pro- 
cedure, the improvement in the parses achieved by 
using this method along with other disambiguation 
methods involving discourse information, as shown 
in another paper (Nasukawa, 1995), shortened the 
TAT in the late stages of the translation procedure, 
45 
Table 4: Results of completing incomplete parses on the basis of discourse information 
Text i Text 2 
Number of sentences in discourse 175 354 
Incomplete parses 32 31 
Unified into a single parse 18 (56.3%) 17 (54.8%) 
Improvement 
in 
translation 
Better 
Even 10 7 
Worse 1 3 
Partially joined or restructured 
'" Improvement Better 
in Even 
translation Worse 
12 (37.5%) 8 (25.8%) 
4 2 
7 3 
1 3 
Not changed 2 (6.3%) 6 (19.4%) 
and compensated for the extra TAT required as a 
result of using the discourse information, provided 
the size of the discourse was kept to between 100 
and 300 sentences. 
In this paper, the term "discourse" is used as a 
set of words in a text together with the usage of 
each of those words in that text - namely, a part 
of speech and modifiee-modifier relationships with 
other words. The basic idea of our method is to im- 
prove the accuracy of sentence analysis simply by 
maintaining consistency in the usage of morphologi- 
cally identical words within the same text. Thus, the 
effectiveness of this method is highly dependent on 
the source text, since it presupposes that morpholog- 
ically identical words are likely to be repeated in the 
same text. However, the results have been encourag- 
ing at least with technical documents such as com- 
puter manuals, where words with the same lemma 
are frequently repeated in a small area of text. More- 
over, our method improves the translation accuracy, 
especially for frequently repeated phrases, which are 
usually considered to be important, and leads to an 
improvement in the overall accuracy of the natural 
language processing system. 
Acknowledgements 
I would like to thank Michael McDonald for in- 
valuable help in proofreading this paper. I would 
also like to thank Taijiro Tsutsumi, Masayuki Mo- 
rohashi, Koichi Takeda, Hiroshi Maruyama, Hiroshi 
Nomiyama, Hideo Watanabe, Shiho Ogino, and the 
anonymous reviewers for their comments and sug- 
gestions. 

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