Argumentation Mining in Parliamentary Discourse

In parliamentary discourse, politicians expound their beliefs and goals through argumentation, and, to persuade the audience, they communicate their values by highlighting some aspect of an issue, an action which is commonly known as framing. The choices of frames are typically dependent upon the speaker’s ideology. In this proposed doctoral work, we will computationally analyze framing strategies and present a model for discovering the latent structure of framing of real-world issues in Canadian parliamentary discourse

Detection of Sarcasm and Nastiness: New Resources for Spanish Language

The main goal of this work is to provide the cognitive computing community with valuable resources to analyze and simulate the intentionality and/or emotions embedded in the language employed in social media. Specifically, it is focused on the Spanish language and online dialogues, leading to the creation of SOFOCO (Spanish Online Forums Corpus). It is the first Spanish corpus consisting of dialogic debates extracted from social media and it is annotated by means of crowdsourcing in order to carry out automatic analysis of subjective language forms, like sarcasm or nastiness. Furthermore, the annotators were also asked about the context need when taking a decision. In this way, the users’ intentions and their behavior inside social networks can be better understood and more accurate text analysis is possible. An analysis of the annotation results is carried out and the reliability of the annotations is also explored. Additionally, sarcasm and nastiness detection results (around 0.76 F-Measure in both cases) are also reported. The obtained results show the presented corpus as a valuable resource that might be used in very diverse future work.This study was partially funded by the Spanish Government (TIN2014-54288-C4-4-R and TIN2017-85854-C4-3-R) by the European Unions’s H2020 program under grant 769872 and by the National Science Foundation of USA (NSF CISE R1 #1202668

Sketching the vision of the Web of Debates

The exchange of comments, opinions, and arguments in blogs, forums, social media, wikis, and review websites has transformed the Web into a modern agora, a virtual place where all types of debates take place. This wealth of information remains mostly unexploited: due to its textual form, such information is difficult to automatically process and analyse in order to validate, evaluate, compare, combine with other types of information and make it actionable. Recent research in Machine Learning, Natural Language Processing, and Computational Argumentation has provided some solutions, which still cannot fully capture important aspects of online debates, such as various forms of unsound reasoning, arguments that do not follow a standard structure, information that is not explicitly expressed, and non-logical argumentation methods. Tackling these challenges would give immense added-value, as it would allow searching for, navigating through and analyzing online opinions and arguments, obtaining a better picture of the various debates for a well-intentioned user. Ultimately, it may lead to increased participation of Web users in democratic, dialogical interchange of arguments, more informed decisions by professionals and decision-makers, as well as to an easier identification of biased, misleading, or deceptive arguments. This paper presents the vision of the Web of Debates, a more human-centered version of the Web, which aims to unlock the potential of the abundance of argumentative information that currently exists online, offering its users a new generation of argument-based web services and tools that are tailored to their real needs

한국어 텍스트 논증 구조의 자동 분석 연구

학위논문 (석사)-- 서울대학교 대학원 : 언어학과 언어학전공, 2016. 2. 신효필.최근 온라인 텍스트 자료를 이용하여 대중의 의견을 분석하는 작업이 활발히 이루어지고 있다. 이러한 작업에는 주관적 방향성을 갖는 텍스트의 논증 구조와 중요 내용을 파악하는 과정이 필요하며, 자료의 양과 다양성이 급격히 증가하면서 그 과정의 자동화가 불가피해지고 있다. 본 연구에서는 정책에 대한 찬반 의견으로 구성된 한국어 텍스트 자료를 직접 구축하고, 글을 구성하는 기본 단위들 사이의 담화 관계의 유형을 정의하였다. 하나의 맥락 안에서 두 개의 문장 혹은 절이 서로 관계를 갖는지, 관계를 갖는다면 서로 동등한 관계인지, 그렇지 않은 경우 어느 문장(절)이 더 중요한 부분으로서 다른 하나의 지지를 받는지의 기준에 따라 담화 관계를 두 개의 층위로 나누어 이용하였다. 이러한 기본 단위들 사이의 관계는 기계 학습과 규칙 기반 방식을 이용하여 예측된다. 이 때 각 글의 저자가 표현하고자 하는 의도, 자신의 주장을 뒷받침하기 위해 제시하는 근거의 종류, 그리고 그 근거를 이루는 논증 전략 등이 텍스트의 언어적 특징과 함께 중요한 자질로 작용된다. 논증의 전략으로는 예시, 인과, 세부 사항에 대한 설명, 반복 서술, 정정, 배경 지식 제공 등이 관찰되었다. 이들 세부 분류는 담화 관계의 대분류를 구성하고, 그 담화 관계를 예측하는 데 쓰이는 자질의 기반이 되었다. 또한 일부 언어적 자질들은 기존 연구를 참고하여 한국어 자료에 적용할 수 있는 형태로 재구성하였다. 이를 이용하여 한국어 코퍼스를 구축하고 한국어 연구에 특화된 접속사 및 연결어의 목록을 구성하여 자질 목록에 포함시켰다. 이러한 자질들에 기반해서 담화 관계를 예측하는 과정을 이 연구에서 독자적인 모델로서 자동화하여 제안하였다. 예측 실험의 결과를 보면 본 연구에서 정의하여 이용한 자질들은 긍정적인 상호 작용을 통해 담화 관계 예측의 성능을 향상시킨다는 것을 알 수 있었다. 그 중에서도 일부 접속사 및 연결어, 문장 성분의 유무에 따른 의존적인 문장 구조, 그리고 같은 내용을 반복 서술하는지의 여부 등이 특히 예측에 기여하였다. 텍스트를 이루는 기본 단위들 사이에 존재하는 담화 관계들은 서로 연결, 합성되어 텍스트 전체에 대응되는 트리 형태의 논증 구조를 이룬다. 이렇게 얻은 논증 구조에 대해서는, 트리의 가장 위쪽인 루트 노드에 글의 주제문이 위치하고, 그 바로 아래 층위에 해당하는 문장(절)들이 근거로서 가장 중요한 내용을 담고 있다고 가정할 수 있다. 따라서 주제문을 직접적으로 뒷받침하는 문장(절)을 추출하면 글의 중요 내용을 얻게 된다. 이는 곧 텍스트 요약 작업에서 유용하게 쓰이는 방식이 될 수 있다. 또한 주제에 따른 입장 분류나 근거 수집 등 다양한 분야에서도 응용이 가능할 것이다.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 초록 117Maste