言語処理学会 第30回年次大会 発表論文集（2024年3月）

Applying mutual information to extract legal domain-specific collocation
nouns in Mandarin
Huang, Po-Hsuan1 , Huang, Sieh-Chuen1, Shao, Hsuan-Lei1
Graduate Institute of Linguistics, National Taiwan University
2
College of Law, National Taiwan University
32
Department of East Asian Studies, National Taiwan Normal University
bensono32169@gmail.com, scchuang@ntu.edu.tw, hlshao2@gmail.com
1

Segmentation constitutes a fundamental process
within of Natural Language Processing (NLP) for theChinese language. However, when we apply it into

primarily in the form of Chinese characters (hànzì/Mandarin), each representing a morpheme, word, or partof a word.
Such a task is especially important and challenging in
Mandarin due to the lack of obvious word boundaries indomain-specific field, it can be limited with the“domain-specific” words. especially in Chinesecharacters. The researchers often used the personal
dictionary by human-hand before, we present a generalsolution of novel collocation extraction technique aimed
at domain-specific texts through iterated segmentation

Chinese orthography and its inherent nature of beinghighly compositional. While many segmentation tools,
such as jieba [2] and ckip [3], can identify small-unitcommon collocations, their performances are largely
affected when faced with domain-specific documents.
Domain-specific larger-unit collocations often fail to be

based on mutual information measure and averaged
mutual information. It has been found that while
mutual-information-based collocation extractions did notbenefit from iterated segmentation, collocation
extractions based on averaged mutual information
performed better after several times of iterated

For example,
Table 1. Comparation of Chinese Segmentation Engine

Abstractsegmentation. Also, while segmentation based on mutual
information reached generally higher precision,
non-collocations extracted with mutual information had
generally larger edit distances than those extracted withaveraged mutual information.

segmentation
judgement text
engineOriginal Text

補為目的，故與損害賠償請求權不同。

Jieba

不當/得利/請求權/，/係/以/使得/利人返/還其/所/受利得/為/目的/，/非以/相/對/人/所受/損害/之/填補為/目的/，/故/與/損害/賠償/請/求權/不同/。

ckip

不當/得利/請求權/，/係/以/使得/利人/返還/其/所/受/利得/為/目的/，/非/以/相對人/所/受/損害/之/填補/為/目的/，/故/與/損害/賠償/請求權/不同/。

1 Introduction
Segmentation as an essential process of Natural
Language Processing (NLP) for the Chinese language.
This assertion derives its significance from the inherent
characteristics of the Chinese script, which is primarily
logographic rather than alphabetic. Unlike languages
utilizing alphabetic scripts, where words are typicallyseparated by spaces or distinct characters, Chinese text
does not possess such explicit word boundaries. Instead,
it employs a continuous sequence of characters,

不當得利請求權，係以使得利人返還其所受利得為目的，非以相對人所受損害之填

不當得利/請求權/，係/以/使/得利人/返還
by legal expert /其/所受利得/為/目的/，非/以/相對人/所(human)
受損害/之/填補/為/目的/，故/與/損害賠償/請求權/不同/。
Resourse: Taiwan Supreme Court Judgment No. 3398 of1973.

― 1329 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

The performance of commonly utilized software
packages for general tasks is commendable. However,researchers often require specialized expertise in theirrespective domains. For instance, the term "不當得利"(condictio, unjust enrichment, ungerrechtfertigte) is athe changes in the frequency distribution at different
levels of segmentation.

proper noun that should be regarded as a cohesive
concept rather than dissected into "不當/得利." Anothererror lies in "使得/利人返" or "使得/利人" as suggested
by ckip, both of which are incorrect, while "得利人"(beneficiary) is the accurate term.
Many of these "legal proper nouns" are collocations

In this study, a corpus consisting of 100,000 legal
judgments (LC) ruled by Taiwanese courts was used.
The documents were first preprocessed and thensegmented into words with ckip.

formed by combining two or more nouns, yet their
meanings transcend simple juxtaposition, often carrying
distinct legal connotations. Typically, experts engage inmanual efforts to construct specialized dictionaries
tailored to their research needs. However, this approach
consumes considerable time and labor, and updating it

To perform iterated segmentation, the mutual
information (MI) and averaged mutual information(AMI) of each pair of bigrams were calculated as in (1)and (2), where P(X) and P(Y) are the probabilities of X
and Y, E[MI(X,Y)] stands for the expectation of themutual information of X and Y, and H(X) and H(Y) standwith new content poses challenges. Consequently, it is
not a universally applicable solution, as far as ourknowledge extends.
Therefore, identifying collocations is an important partof preprocessing for multiple natural language
processing applications, including word sense

for the entropies of X and Y.

disambiguation, machine translation, and information
retrieval, etc. [1]. identified, resulting in less-than-ideal
performances for subsequent tasks. This study thereforeseeks to examine collocation extraction methods suitablefor domain-specific texts in Mandarin.
While several past studies have proposed different

segmentation threshold. To determine the segmentationthreshold, the averaged numbers of words per sentence at
different thresholds were calculated starting from 0 to
when the averaged numbers of words stopped increasing(i.e., no words were segmented into a larger unit), with astep of 1 for MI and 0.001 for AMI. An illustration is

collocation extraction methods in Mandarin (e.g., [4, 5, 6,7]), these methods all required the additional
involvement of dictionaries or part-of-speech tags. While
such methods are viable when dealing with common
texts, a domain-specific dictionary is often unobtainable,and part-of-speech tagging also often fails when faced

shown in Fig. 1. The elbow method was then used todetermine the optimal segmentation threshold. The
segmented words then underwent a new round ofiteration, where the (A)MI values were recalculated.
There was a total of 10 iterations.

2 Methods
2.1 Corpora

2.2 Iterated Segmentation Based on Mutual
Information and Averaged Mutual Information

In each iteration, word boundaries were determined at
bigrams with an (A)MI value lower than thewith domain-specific texts. As such, a purely
association-rule-based method would be a more feasible
solution for automatic domain-specific collocation
extraction in Mandarin.
In this paper, we propose a novel technique for
automatedcollocation
extraction
aimedatdomain-specific texts. Specifically, we combine andcompare two association measures, i.e., mutualinformation and its variant, averaged mutual information,with iterated segmentation, in an attempt to account for― 1330 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

Figure 1. An example of the distribution of the mean
numbers of words per sentence across different levels ofMI threshold. The mean number of words stopped
increasing at an MI threshold near 1500.

2.3 Evaluation

collocations, the extractions based on MI performed
worse as the level of iterations increased; on the flip side,
the precisions of the extractions based on AMI increased
with the level of iteration.
The non-iterated groups, on the other hand, were less

To compare the interaction of different association
measures and iterated segmentation, the extracted
collocations after each iteration were evaluated (namedMI-iterated 1–10 and AMI-iterated 1–10). To comparethem with segmentation without iteration, 10 sets ofcollocations were additionally extracted without iteration,affected by the level of iteration. Specifically, the MIgroups did not seem to be affected by the level ofiteration, with the precision scores staying at 0.63 to 0.64
for legal collocations and 0.74 to 0.75 for generalcollocations throughout. On the other hand, the AMI
groups performed better as the level of iteration

with segmentation thresholds based on the mean
numbers of words at each level of the 10 iterations(named MI-noniterated 1–10 and AMI-noniterated 1–10).
That is, the mean numbers of words of the noniterated
groups were the same as their iterated counterparts. For
instance, if the mean number of words for MI-iterated 5increased. The precisions, however, stopped increasing
after it reached 0.63 to 0.64 for legal collocations and0.68 to 0.69 for general collocations as well.
Overall, the groups with the highest precisions wereMI-iterated
1,MI-noniterated
1–10,andAMI-noniterated 7–10. For all groups, general

was 3.5, then the collocations MI-noniterated 5 would beextracted based on the segmentation threshold at which
the mean number of words for MI-noniterated was also
3.5. This was done to ensure the comparability of theiterated groups and their counterparts at each iterationlevel by making sure that they had the same mean

collocation extractions had higher precision scores thanlegal collocation extractions.

numbers of words.
The extracted collocation candidates were thenmanually examined by four legal professionals. The
candidates were labeled as three types: 1) legal
collocations, 2) general collocations, and 3)
non-collocations. Following [8], label ranking average

Figure 2. Label ranking average precision scores of thelegal collocations (solid line) and general collocations

precision (LRAP) scores were used to evaluate theprecisions of the extracted collocation candidates.
Additionally, for candidates judged as non-collocations,the correct target collocations were also labeled by theexaminers. Levenshtein distances were calculated
between the non-collocations and the target collocations(dashed line).

to estimate their similarities.

AMI-based extractions can be observed. In general,AMI-based extractions exhibited less edit distance
between the false collocations and the target collocations
than MI-based extractions. More importantly, whileMI-based extractions, once again, did not benefit fromiteration, the edit distances of AMI-based extractions

3 Results
3.1 Label Ranking Average Precision Scores
The LRAP scores of legal collocations and generalcollocations extracted with (A)MI-iterated and(A)MI-noniterated 1–10 are shown in Fig. 2. As can beseen, an interaction between the different association
measures and iterated segmentation was present.
Specifically, for both legal collocations and general

3.2

Levenshtein
Distance
betweenNon-collocations and Target Collocations
The Levenshtein distances between non-collocations
and target collocations for different extractions areshown in Fig. 3. An obvious difference between MI- anddecreased as the iteration level increased at the earlier
stages of the iteration.

― 1331 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

better performing. However, the edit distances of theAMI-based extractions were lower than MI-basedextractions. Specifically, the edit distances of AMI-based
extractions decreased as the iteration level increased at
the earlier stages of the iteration. This suggests that
AMI-based extractions may be a better choice if thepurpose is to not only reach higher precision but also
reduce the edit distances with the target collocations.
Figure 3. Levenshtein distance between non-collocations
and target collocations.

4 Discussion and Conclusion

4.3

Performanceceilingofpurely
association-measure-based extractions
Another issue that is worthy of discussion is theassociation measures and the level of iteration
In Section 3.1, it has been found that MI andAMI-based extractions reacted to iteration differently.
MI-based extractions did not benefit from iteration,

performance ceiling of the extraction methods
investigated in this study. In Fig. 2, it can be observedthat whether it be iterated or non-iterated and MI- orAMI-based, the precisions scores for the legal
collocations seemed to stop increasing at a certain level(0.64). This might suggest that there exists an inherent

while AMI-based extractions increased in precision as
the level of iteration increased. This might be due to thenature of AMI and its difference with MI. While MImeasures the probability of two events happening
together, AMI additionally takes into consideration theprobabilities of one and both of the two events not

limittotheperformanceofpurely
association-measure-based extractions. Indeed, in paststudies, most collocation extraction methods require acombination of association measures and the additional
involvement of dictionaries or part-of-speech tags.
Extractions with high precisions may therefore be lesshappening. AMI therefore takes into account not merely
the probability of the occurrence of a certain bigram, butalso the counter-factual dependence of the two elementsin the bigram, where the absence/presence of one
element may promote the presence/absence of the otherelement. AMI-based extractions may therefore be moreattainable with purely association-measure-based
methods. Alternatively, such a limitation may alsosurface from the relatively smaller sizes of the corpora
used in this study, and the potential word segmentation
errors during the initial segmentation process of thecorpora. A larger corpus may disperse this question.

sensitive to the change in probabilities of theco-occurrence as well as the counterfactual dependenceof the elements in a given bigram after each iteration
than MI-based extractions, while MI-based extractions
may erroneously combine bigrams into collocations after
several iterations without taking into account such

4.4 Conclusion

counterfactual dependence.

those based on averaged mutual information benefited
from iterated segmentation, though there seems to be aperformance ceiling. Specifically, averaged mutual
information has been found to reduce the edit distances
between non-collocations and target collocations. Theauthors hope to provide further insights into the use of

4.1 The interaction between the precisions of

4.2 Comparison of the performances between
MI- and AMI-based extractions
Another issue worth discussing is the performances ofthe MI- and AMI-based extractions. The precision scoresof MI-based extractions are higher than AMI-based onesat the previous stages of the iteration, and are close toAMI-based ones at higher iteration levels. As such,judging from precision scores, an extraction based on MI
without extraction seems to be both more efficient andIn this study, a novel association rule, i.e., averaged
mutual information, and the use of iterated segmentation
have been explored for domain-specific collocation
extraction in Mandarin. It has been shown that comparedwith extractions based on canonical mutual information,

association rules in information retrieval, and to shedlight on the issue of domain-specific collocationextraction.

― 1332 ―This work is licensed by the author(s) under CC BY 4.0(https://creativecommons.org/licenses/by/4.0/).

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