Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics.

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113407/1/rahuljha_1.pd

Argumentative zoning information extraction from scientific text

Let me tell you, writing a thesis is not always a barrel of laughs—and strange things can happen, too. For example, at the height of my thesis paranoia, I had a re-current dream in which my cat Amy gave me detailed advice on how to restructure the thesis chapters, which was awfully nice of her. But I also had a lot of human help throughout this time, whether things were going fine or beserk. Most of all, I want to thank Marc Moens: I could not have had a better or more knowledgable supervisor. He always took time for me, however busy he might have been, reading chapters thoroughly in two days. He both had the calmness of mind to give me lots of freedom in research, and the right judgement to guide me away, tactfully but determinedly, from the occasional catastrophe or other waiting along the way. He was great fun to work with and also became a good friend. My work has profitted from the interdisciplinary, interactive and enlightened atmosphere at the Human Communication Centre and the Centre for Cognitive Science (which is now called something else). The Language Technology Group was a great place to work in, as my research was grounded in practical applications develope

Learning Analytics for Academic Writing through Automatic Identification of Meta-discourse

Effective written communication is an essential skill which promotes educational success for undergraduates. Argumentation is a key requirement of successful writing, which is the most common genre that undergraduates have to write particularly in the social sciences. Therefore, when assessing student writing academic tutors look for students’ ability to present and pursue well-reasoned and strong arguments through scholarly argumentation, which is articulated by meta-discourse. Today, there are some natural language processing systems which automatically detect authors’ rhetorical moves in scholarly texts. Hence, when assessing their students’ essays, educators could benefit from the available automated textual analysis which can detect meta-discourse. However, previous work has not shown whether these technologies can be used to analyse student writing reliably. The aim of this thesis therefore has been to understand how automated analysis of meta-discourse in student writing can be used to support tutors’ essay assessment practices. This thesis evaluates a particular language analysis tool, the Xerox Incremental Parser (XIP) as an exemplar of this type of automated technology. The studies presented in this thesis investigates how tutors define the quality of undergraduate writing and suggests key elements that make for good quality student writing in the social sciences, where XIP seems to work best. This thesis also sets out the changes that needs to be made to the XIP and proposes in what ways its output can be delivered to tutors so that they make use of this output to give feedback on student essays. The findings reported also show problems that academic tutors experience in essay assessment, which potentially could be solved by automated support. However, tutors have preconceptions about the use of automated support. The study revealed that tutors want to be assured that they retain the ‘power’ themselves in any decision of using automated support to overcome these preconceptions

Expert and Corpus-Based Evaluation of a 3-Space Model of Conceptual Blending

This paper presents the 3-space model of conceptual blending that estimates the figurative similarity between Input spaces 1 and 2 using both their analogical similarity and the interconnecting Generic Space. We describe how our Dr Inventor model is being evaluated as a model of lexically based figurative similarity. We describe distinct but related evaluation tasks focused on 1) identifying novel and quality analogies between computer graphics publications 2) evaluation of machine generated translations of text documents 3) evaluation of documents in a plagiarism corpus. Our results show that Dr Inventor is capable of generating novel comparisons between publications but also appears to be a useful tool for evaluating machine translation systems and for detecting and assessing the level of plagiarism between documents. We also outline another more recent evaluation, using a corpus of patent applications

Synonymy and Polysemy in Legal Terminology and Their Applications to Bilingual and Bijural Translation

The paper focuses on synonymy and polysemy in the language of law in English-speaking countries. The introductory part briefly outlines the process of legal translation and tackle the specificity of bijural translation. Then, traditional understanding of what a term is and its application to legal terminology is considered; three different levels of vocabulary used in legal texts are outlined and their relevance to bijural translation explained. Next, synonyms in the language of law are considered with respect to their intension and distribution, and examples are given to show that most expressions or phrases which are interchangeable synonyms in the general language should be treated carefully in legal translation. Finally, polysemes in legal terminology are discussed and examples given to illustrate problems potentially encountered by translators

A Graph-Based Approach for the Summarization of Scientific Articles

Automatic text summarization is one of the eminent applications in the field of Natural Language Processing. Text summarization is the process of generating a gist from text documents. The task is to produce a summary which contains important, diverse and coherent information, i.e., a summary should be self-contained. The approaches for text summarization are conventionally extractive. The extractive approaches select a subset of sentences from an input document for a summary. In this thesis, we introduce a novel graph-based extractive summarization approach. With the progressive advancement of research in the various fields of science, the summarization of scientific articles has become an essential requirement for researchers. This is our prime motivation in selecting scientific articles as our dataset. This newly formed dataset contains scientific articles from the PLOS Medicine journal, which is a high impact journal in the field of biomedicine. The summarization of scientific articles is a single-document summarization task. It is a complex task due to various reasons, one of it being, the important information in the scientific article is scattered all over it and another reason being, scientific articles contain numerous redundant information. In our approach, we deal with the three important factors of summarization: importance, non-redundancy and coherence. To deal with these factors, we use graphs as they solve data sparsity problems and are computationally less complex. We employ bipartite graphical representation for the summarization task, exclusively. We represent input documents through a bipartite graph that consists of sentence nodes and entity nodes. This bipartite graph representation contains entity transition information which is beneficial for selecting the relevant sentences for a summary. We use a graph-based ranking algorithm to rank the sentences in a document. The ranks are considered as relevance scores of the sentences which are further used in our approach. Scientific articles contain reasonable amount of redundant information, for example, Introduction and Methodology sections contain similar information regarding the motivation and approach. In our approach, we ensure that the summary contains sentences which are non-redundant. Though the summary should contain important and non-redundant information of the input document, its sentences should be connected to one another such that it becomes coherent, understandable and simple to read. If we do not ensure that a summary is coherent, its sentences may not be properly connected. This leads to an obscure summary. Until now, only few summarization approaches take care of coherence. In our approach, we take care of coherence in two different ways: by using the graph measure and by using the structural information. We employ outdegree as the graph measure and coherence patterns for the structural information, in our approach. We use integer programming as an optimization technique, to select the best subset of sentences for a summary. The sentences are selected on the basis of relevance, diversity and coherence measure. The computation of these measures is tightly integrated and taken care of simultaneously. We use human judgements to evaluate coherence of summaries. We compare ROUGE scores and human judgements of different systems on the PLOS Medicine dataset. Our approach performs considerably better than other systems on this dataset. Also, we apply our approach on the standard DUC 2002 dataset to compare the results with the recent state-of-the-art systems. The results show that our graph-based approach outperforms other systems on DUC 2002. In conclusion, our approach is robust, i.e., it works on both scientific and news articles. Our approach has the further advantage of being semi-supervised