JAIST Repository
https://dspace.jaist.ac.jp/Title

物語テキストを対象とした登場人物の関係抽出

Author(s)Nishihara, Hiromasa

CitationIssue Date

2015‑03

TypeThesis or Dissertation

Text version

authorURL

http://hdl.handle.net/10119/12702

RightsDescription

Supervisor: 白井清昭, 情報科学研究科, 修士

Japan Advanced Institute of Science and Technology

Extraction of Relations among Characters in LiteraryTexts
Hiromasa Nishihara (1310054)
School of Information Science,
Japan Advanced Institute of Science and Technology
February 12, 2015

Keywords: Information Extraction, Relation Extraction, Literary Text,
Acquisition of Pattern.
Many people have a lot of fun for reading novels. Their reading is often
suspended if they have a time only when they take a train to work or havelunch break in an oﬃce. When they resume reading, they sometimes forget
the story of the novel and have to read it again a little. Showing a summaryof information about characters in the novel would help readers to remind
the story and restart their reading smoothly. To support the readers tounderstand the contents of the novel, this research proposes a method toautomatically extract relations among characters in literary texts. Theexamples of character relations are family such as “John is a brother ofJames.” and friendship such as “Michael and Robert are friends.” To
implement and evaluate our proposed method, we use a collection of novels
excerpted from the website ‘Aozora Bunko’ which collects a large amountof copyright expired Japanese novels.
The task of this research can be regarded as a kind of relation extractiontask. Many previous studies aimed at extraction of various relations; notonly relations between people but also general ones such as causal relationand ‘is-a’ relation. Some researches focused on extracting social relations
from the literary text. They tried to just quantify a degree of relationships between the characters or extract only limited types of the relations.
On the other hand, the goal of this research is to extract many kinds ofc 2015 by Hiromasa Nishihara
Copyright ⃝1

relations such as family and friends from the novel. Although a few methods to automatically obtain patterns for extraction of the general relations
have been already proposed, they cannot be simply applied for our task.
Therefore, we proposed an original method to semi-automatically obtain
the patterns to extract the character relations.
The outline of the proposed method is as follows. First, we manually construct a dictionary called ‘Relation Word Dictionary’ that compiles relation
words. In this paper, ‘relation word’ is defined as a word that expresses
a relation between people. We also construct a set of patterns that canextract the relations between the characters. After the above preparation,
preprocessing such as morphological analysis is performed to the text inthe novel. A list of the characters is also built by identifying the charactersin the novel in this preprocessing. In addition, zero anaphora resolution isperformed to compensate ellipses in Japanese sentences. By referring thecharacter list and Relation Word Dictionary, sentences that may express
the relations between the characters (called ‘character relation sentence’hereafter) are extracted. Then, the character relations are extracted bypattern matching. In this study, the character relation to be extracted iseither pair relation such as ‘John - father’ or triplet relation such as ‘John- sister - Mary’. Finally, a diagram of the relations among the charactersis constructed by concatenating the extracted relations.
In the preprocessing, the public tool MeCab and CaboCha are used formorphological analysis and dependency parsing, respectively. The characters in the novel are identified based on a named entity extraction moduleof CaboCha, semantic classes in a thesaurus and selectional restriction ofcase frames. Both semantic classes and case frames are provided by the lexical resource ‘Nihongo Goi Taikei’ (NGT hereafter). In addition, ‘characterstop words’ are used for preventing from identifying wrong words as thecharacters. They are words that do not express a meaning of a person, andmanually collected by checking the frequently appearing characters in 500novels. Since ellipses are often found in Japanese texts, we implement zero
anaphora resolution based on Nariyama’s work with some modifications.
In our zero anaphora resolution method, omitted cases in the sentences
are detected by case frame analysis, and ellipses are filled by several rules.
Relation Word Dictionary is manually developed by referring the two the2sauri, ‘Kadokawa Ruigo Shin-Jiten’ and NGT. Two dictionaries are built:
Relation Word Dictionary A that compiles only the unambiguous relation
words, and Relation Word Dictionary B that compiles both the ambiguous
and unambiguous relation words. Here, ‘ambiguous relation word’ stands
for a word that has meanings of a relation and not a relation. We propose
two approaches to construct a set of patterns for extracting the character
relations: one is manual development and the other is semi-automatic development. In the former approach, 8 patterns are made by examinationof 10 novels. Each is a string-based pattern that includes characters, relation word, postpositions and so on. In the latter approach, the patterns
are designed to extract the characters and relation word from ‘bunsetsu’, achunk of a basic phrase in Japanese, by checking the postposition appearing after the character or relation word as well as dependencies between
two characters or one character and one relation word. In addition, eachpattern is associated with one relation word. At first, 2 templates of patterns for extraction of the pair relation and 12 templates for the triplet
relation are prepared. They are applied to the 500 novels to acquire candidates of patterns. Furthermore, more candidates of patterns are produced
by generalization of the obtained patterns. Then reliability of each candidate of the pattern is estimated. It is defined as the ratio of the numberof the sentences where the correct relation is extracted by the pattern to
the total number of the sentences matched by the pattern. The reliability
is manually calculated by using the 500 novels. Finally, only candidates
whose reliability is higher than a threshold are selected as the patterns.
After extracting the character relations by pattern matching, the diagramof the relations among the characters is constructed. A node represents
the character and an edge represents the relation. The edge of the triplet
relation is labeled with the relation word.
The proposed methods were evaluated by the experiment. Ten novels
were used as a test data. Precision, recall and F-measure of extraction of
the character relations from the test data were measured. The same relations in diﬀerent notations (e.g. ‘David - Father’ and ‘David - Dad’) weremanually merged into a single relation in both the gold standard and theoutput of the proposed system. Comparing the system with and without
zero anaphora resolution, the performance of the latter was slightly bet3

ter. This might be because the performance of zero anaphora resolution
was poor. When the manually constructed patterns were used, the systemusing Relation Word Dictionary A was better than that using B. The best
F-measure was 0.30. On the other hand, when the semi-automatically constructed patterns were used, the system using Relation Word Dictionary
B was better. Comparing the various thresholds for the reliability of thepatterns, it was found that the best threshold was 0.6. F-measure of thesystem with the best threshold was 0.34, which was slightly better thanthe method using manually constructed patterns. Both proposed methods
outperformed the baseline where all possible relations between the characters from ‘character relation sentences’ were extracted without patternmatching. We conducted an error analysis on the proposed methods. The
major causes of false-positive errors were failure of identification of thecharacters in the novel and the use of the inappropriate patterns. On theother hand, the major causes of false-negative errors were failure to retrieve the characters and the lack of the extraction patterns. Although theperformance of the proposed method is not high enough and there is muchroom to improve it, one of the contribution of the paper is to show several
possible solutions via our error analysis. Finally, a diagram of the character
relations was constructed and evaluated. It was far from the ideal one.
In future, the performance of the zero anaphora resolution should be improved. The method to fully automatically construct the character relation
extraction patterns should be investigated, too.

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