Identifying communicative functions in discourse with content types

Texts are not monolithic entities but rather coherent collections of micro illocutionary acts which help to convey a unitary message of content and purpose. Identifying such text segments is challenging because they require a fine-grained level of analysis even within a single sentence. At the same time, accessing them facilitates the analysis of the communicative functions of a text as well as the identification of relevant information. We propose an empirical framework for modelling micro illocutionary acts at clause level, that we call content types, grounded on linguistic theories of text types, in particular on the framework proposed by Werlich in 1976. We make available a newly annotated corpus of 279 documents (for a total of more than 180,000 tokens) belonging to different genres and temporal periods, based on a dedicated annotation scheme. We obtain an average Cohen’s kappa of 0.89 at token level. We achieve an average F1 score of 74.99% on the automatic classification of content types using a bi-LSTM model. Similar results are obtained on contemporary and historical documents, while performances on genres are more varied. This work promotes a discourse-oriented approach to information extraction and cross-fertilisation across disciplines through a computationally-aided linguistic analysis

Persuasion in earnings calls: A diachronic pragmalinguistic analysis

This study investigates persuasive language in earnings calls. These are routine events organized by companies to report their quarterly financial results. The analysis is based on the earnings calls of 10 companies in the third quarter of 2009, when financial markets were still suffering from the global financial crisis, and the third quarter of 2013 when markets had largely recovered. Earnings call transcripts were compiled in two parallel corpora (Crisis Corpus and Recovery Corpus), thus providing a diachronic perspective. Semantic annotation software was used to extract pragmalinguistic resources of persuasion. The Crisis Corpus had a higher frequency of persuasive items, as executives often emphasized progress and future hopes. However, the types of items were largely the same across the corpora. This suggests a well-consolidated linguistic protocol within this discourse community that transcends financial performance. The findings offer insights into how earnings call participants use persuasive language strategically to achieve their distinct professional objectives as responsible providers of information (executives) vs. discerning seekers of information (analysts)

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.Comment: European Chapter of the Association for Computational Linguistics (EACL 2017

Deep Linguistic Processing with GETARUNS for Spoken Dialogue Understanding

In this paper we will present work carried out to scale up the system for text understanding called GETARUNS, and port it to be used in dialogue understanding. The current goal is that of extracting automatically argumentative information in order to build argumentative structure. The long term goal is using argumentative structure to produce automatic summarization of spoken dialogues. Very much like other deep linguistic processing systems, our system is a generic text/dialogue understanding system that can be used in connection with an ontology – WordNet - and other similar repositories of commonsense knowledge. We will present the adjustments we made in order to cope with transcribed spoken dialogues like those produced in the ICSI Berkeley project. In a final section we present preliminary evaluation of the system on two tasks: the task of automatic argumentative labeling and another frequently addressed task: referential vs. non-referential pronominal detection. Results obtained fair much higher than those reported in similar experiments with machine learning approaches

Anticipating resistance to health advice: A speech act perspective

This article addresses the problem of resistance to health advice. It is argued that potential criticism against advice in health settings can be systematically defined with the help of the felicity conditions of the speech act of advising. By taking into account the setting in which health advice is delivered, specified conditions for advising in health settings are proposed. The objective of this study is to present a systematic overview of relevant criticism showing what issues could provoke resistance to advice and thus need to be anticipated or answered. The relevance of these points is illustrated in a case study of advice on achieving a healthy weight on webpages from the U.S. Centers for Disease Control and Prevention. A content analysis of the webpages shows that each of the possible points of criticism can be dealt with to prevent resistance

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