4 research outputs found
νκ΅μ΄ ν μ€νΈ λ Όμ¦ κ΅¬μ‘°μ μλ λΆμ μ°κ΅¬
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μΈμ΄νκ³Ό μΈμ΄νμ 곡, 2016. 2. μ ν¨ν.μ΅κ·Ό μ¨λΌμΈ ν
μ€νΈ μλ£λ₯Ό μ΄μ©νμ¬ λμ€μ μ견μ λΆμνλ μμ
μ΄ νλ°ν μ΄λ£¨μ΄μ§κ³ μλ€. μ΄λ¬ν μμ
μλ μ£Όκ΄μ λ°©ν₯μ±μ κ°λ ν
μ€νΈμ λ
Όμ¦ ꡬ쑰μ μ€μ λ΄μ©μ νμ
νλ κ³Όμ μ΄ νμνλ©°, μλ£μ μκ³Ό λ€μμ±μ΄ κΈκ²©ν μ¦κ°νλ©΄μ κ·Έ κ³Όμ μ μλνκ° λΆκ°νΌν΄μ§κ³ μλ€.
λ³Έ μ°κ΅¬μμλ μ μ±
μ λν μ°¬λ° μ견μΌλ‘ ꡬμ±λ νκ΅μ΄ ν
μ€νΈ μλ£λ₯Ό μ§μ ꡬμΆνκ³ , κΈμ ꡬμ±νλ κΈ°λ³Έ λ¨μλ€ μ¬μ΄μ λ΄ν κ΄κ³μ μ νμ μ μνμλ€. νλμ λ§₯λ½ μμμ λ κ°μ λ¬Έμ₯ νΉμ μ μ΄ μλ‘ κ΄κ³λ₯Ό κ°λμ§, κ΄κ³λ₯Ό κ°λλ€λ©΄ μλ‘ λλ±ν κ΄κ³μΈμ§, κ·Έλ μ§ μμ κ²½μ° μ΄λ λ¬Έμ₯(μ )μ΄ λ μ€μν λΆλΆμΌλ‘μ λ€λ₯Έ νλμ μ§μ§λ₯Ό λ°λμ§μ κΈ°μ€μ λ°λΌ λ΄ν κ΄κ³λ₯Ό λ κ°μ μΈ΅μλ‘ λλμ΄ μ΄μ©νμλ€.
μ΄λ¬ν κΈ°λ³Έ λ¨μλ€ μ¬μ΄μ κ΄κ³λ κΈ°κ³ νμ΅κ³Ό κ·μΉ κΈ°λ° λ°©μμ μ΄μ©νμ¬ μμΈ‘λλ€. μ΄ λ κ° κΈμ μ μκ° νννκ³ μ νλ μλ, μμ μ μ£Όμ₯μ λ·λ°μΉ¨νκΈ° μν΄ μ μνλ κ·Όκ±°μ μ’
λ₯, κ·Έλ¦¬κ³ κ·Έ κ·Όκ±°λ₯Ό μ΄λ£¨λ λ
Όμ¦ μ λ΅ λ±μ΄ ν
μ€νΈμ μΈμ΄μ νΉμ§κ³Ό ν¨κ» μ€μν μμ§λ‘ μμ©λλ€. λ
Όμ¦μ μ λ΅μΌλ‘λ μμ, μΈκ³Ό, μΈλΆ μ¬νμ λν μ€λͺ
, λ°λ³΅ μμ , μ μ , λ°°κ²½ μ§μ μ 곡 λ±μ΄ κ΄μ°°λμλ€. μ΄λ€ μΈλΆ λΆλ₯λ λ΄ν κ΄κ³μ λλΆλ₯λ₯Ό ꡬμ±νκ³ , κ·Έ λ΄ν κ΄κ³λ₯Ό μμΈ‘νλ λ° μ°μ΄λ μμ§μ κΈ°λ°μ΄ λμλ€.
λν μΌλΆ μΈμ΄μ μμ§λ€μ κΈ°μ‘΄ μ°κ΅¬λ₯Ό μ°Έκ³ νμ¬ νκ΅μ΄ μλ£μ μ μ©ν μ μλ ννλ‘ μ¬κ΅¬μ±νμλ€. μ΄λ₯Ό μ΄μ©νμ¬ νκ΅μ΄ μ½νΌμ€λ₯Ό ꡬμΆνκ³ νκ΅μ΄ μ°κ΅¬μ νΉνλ μ μμ¬ λ° μ°κ²°μ΄μ λͺ©λ‘μ ꡬμ±νμ¬ μμ§ λͺ©λ‘μ ν¬ν¨μμΌ°λ€. μ΄λ¬ν μμ§λ€μ κΈ°λ°ν΄μ λ΄ν κ΄κ³λ₯Ό μμΈ‘νλ κ³Όμ μ μ΄ μ°κ΅¬μμ λ
μμ μΈ λͺ¨λΈλ‘μ μλννμ¬ μ μνμλ€.
μμΈ‘ μ€νμ κ²°κ³Όλ₯Ό 보면 λ³Έ μ°κ΅¬μμ μ μνμ¬ μ΄μ©ν μμ§λ€μ κΈμ μ μΈ μνΈ μμ©μ ν΅ν΄ λ΄ν κ΄κ³ μμΈ‘μ μ±λ₯μ ν₯μμν¨λ€λ κ²μ μ μ μμλ€. κ·Έ μ€μμλ μΌλΆ μ μμ¬ λ° μ°κ²°μ΄, λ¬Έμ₯ μ±λΆμ μ 무μ λ°λ₯Έ μμ‘΄μ μΈ λ¬Έμ₯ ꡬ쑰, κ·Έλ¦¬κ³ κ°μ λ΄μ©μ λ°λ³΅ μμ νλμ§μ μ¬λΆ λ±μ΄ νΉν μμΈ‘μ κΈ°μ¬νμλ€.
ν
μ€νΈλ₯Ό μ΄λ£¨λ κΈ°λ³Έ λ¨μλ€ μ¬μ΄μ μ‘΄μ¬νλ λ΄ν κ΄κ³λ€μ μλ‘ μ°κ²°, ν©μ±λμ΄ ν
μ€νΈ μ 체μ λμλλ νΈλ¦¬ ννμ λ
Όμ¦ ꡬ쑰λ₯Ό μ΄λ£¬λ€. μ΄λ κ² μ»μ λ
Όμ¦ ꡬ쑰μ λν΄μλ, νΈλ¦¬μ κ°μ₯ μμͺ½μΈ λ£¨νΈ λ
Έλμ κΈμ μ£Όμ λ¬Έμ΄ μμΉνκ³ , κ·Έ λ°λ‘ μλ μΈ΅μμ ν΄λΉνλ λ¬Έμ₯(μ )λ€μ΄ κ·Όκ±°λ‘μ κ°μ₯ μ€μν λ΄μ©μ λ΄κ³ μλ€κ³ κ°μ ν μ μλ€. λ°λΌμ μ£Όμ λ¬Έμ μ§μ μ μΌλ‘ λ·λ°μΉ¨νλ λ¬Έμ₯(μ )μ μΆμΆνλ©΄ κΈμ μ€μ λ΄μ©μ μ»κ² λλ€. μ΄λ 곧 ν
μ€νΈ μμ½ μμ
μμ μ μ©νκ² μ°μ΄λ λ°©μμ΄ λ μ μλ€. λν μ£Όμ μ λ°λ₯Έ μ
μ₯ λΆλ₯λ κ·Όκ±° μμ§ λ± λ€μν λΆμΌμμλ μμ©μ΄ κ°λ₯ν κ²μ΄λ€.These days, there is an increased need to analyze mass opinions using on-line text data. These tasks need to recognize the argumentation schemes and main contents of subjective, argumentative writing, and the automatization of the required procedures is becoming indispensable.
This thesis constructed the text data using Korean debates on certain political issues, and defined the types of discourse relations between basic units of text segments. The discourse relations are classified into two levels and four subclasses, according to the standards which determine whether the two segments are related to each other in a context, whether the relation is coordinating or subordinating, and which of the two units in a pair is supported by the other as a more important part.
The relations between basic text units are predicted based on machine learning and rule-based methods. The features for the prediction of discourse relations include what the author of a text wants to claim and argumentative strategies comprising grounds for the author's claim, using linguistic properties shown in texts. The strategies for argument are observed and subcategorized into Providing Examples, Cause-and-Effects, Explanations in Detail, Restatements, Contrasts, Background Knowledge, and more. These subclasses compose a broader class of discourse relations and became the basis for features used during the classification of the relations.
Some linguistic features refer to those of previous studies, they are reconstituted in a revised form which is more appropriate for Korean data. Thus, this study constructed a Korean debate corpus and a list of connectives specialized to deal with Korean texts to include in the experiment features. The automated prediction of discourse relations based on those features is suggested in this study as a unique model of argument mining.
According to the results of experiments predicting discourse relations, the features defined and used in this study are observed to improve the performance of prediction tasks through positive interactions with each other. In particular, some explicit connectives, dependent sentence structures based on lack of certain components, and whether the same meanings are restated clearly contributed to the classification tasks.
The discourse relations between basic text units are related and combined with each other to comprise a tree-form argumentation structure for the overall document. Regarding the argumentation structure, the topic sentence of the document is located at the root node in the tree, and it is assumed that the nodes of sentences or clauses right below the root node contain the most important contents as grounds for the topic unit. Therefore, extraction of the text segments directly supporting the topic sentence may help in obtaining the important contents in each document. This can be one of the useful methods in text summarization. Additionally, applications to various fields may also be possible, including stance classification of debate texts, extraction of grounds for certain topics, and so on.1 Introduction 1
1.1 Purposes 1
1.1.1 A Study of Korean Texts with Linguistic Cues 1
1.1.2 Detection of Argumentation Schemes in Debate Texts 2
1.1.3 Extraction of Important Content in Argumentation Schemes of Texts 2
1.2 Structure 3
2 Previous Work 5
2.1 Argumentation Mining Tasks 7
2.1.1 Argument Elements 7
2.1.2 Argumentation Schemes 9
2.2 Argumentation Schemes in Various Texts 14
2.2.1 Dialogic vs. Monologic Texts 14
2.2.2 Debate Texts vs. Other Texts 15
2.2.3 Studies in Other Languages 17
2.3 Theoretical Basis 18
2.3.1 Argumentation Theory 18
2.3.2 Discourse Theory 21
3 Identifying Argumentation Schemes in Debate Texts 25
3.1 Data Description 25
3.2 Basic Units 27
3.3 Discourse Relations 29
3.3.1 Strategies for Proving a Claim 29
3.3.2 Definition 35
4 Automatic Identification of Argumentation Schemes 41
4.1 Annotation 41
4.2 Baseline 46
4.3 Proposed Model 50
4.3.1 O vs. X Classification 51
4.3.2 Convergent Relation Rule 61
4.3.3 NN vs. NS vs. SN Classification 65
4.4 Evaluation 67
4.4.1 Measures 67
4.4.2 Results 68
4.5 Discussion 74
4.6 A Pilot Study on English Texts 81
5 Detecting Important Units 87
6 Conclusion 99
Bibliography 103
μ΄λ‘ 117Maste
Procedurally Rhetorical Verb-Centric Frame Semantics as a Knowledge Representation for Argumentation Analysis of Biochemistry Articles
The central focus of this thesis is rhetorical moves in biochemistry
articles. Kanoksilapatham has provided a descriptive theory of
rhetorical moves that extends Swales' CARS model to the complete
biochemistry article. The thesis begins the construction of a computational
model of this descriptive theory. Attention is placed on the Methods
section of the articles. We hypothesize that because authors' argumentation
closely follows their experimental procedure, procedural verbs may
be the guide to understanding the rhetorical moves. Our work proposes
an extension to the normal (i.e., VerbNet) semantic roles especially
tuned to this domain. A major contribution is a corpus of Method sections
that have been marked up for rhetorical moves and semantic roles.
The writing style of this genre tends to occasionally omit semantic
roles, so another important contribution is a prototype ontology
that provides experimental procedure knowledge for the biochemistry
domain. Our computational model employs machine learning to build its
models for the semantic roles and rhetorical moves, validated against
a gold standard reflecting the annotation of these texts by human experts.
We provide significant insights into how to derive these annotations,
and as such have contributions as well to
the general challenge of producing markups in the domain
of biomedical science documents, where specialized knowledge is required
Legal Knowledge and Information Systems - JURIX 2017: The Thirtieth Annual Conference
The proceedings of the 30th International Conference on Legal Knowledge and Information Systems β JURIX 2017. For three decades, the JURIX conferences have been held under the auspices of the Dutch Foundation for Legal Knowledge Based Systems (www.jurix.nl). In the time, it has become a European conference in terms of the diverse venues throughout Europe and the nationalities of
participants