This short paper presents a first implementation of a knowledge-driven argument mining approach. The major processing steps and language resources of the system are surveyed. An indicative evaluation outlines challenges and improvement directions.

In most international industries, English is the main language of communication for technical documents. These documents are designed to be as unambiguous as possible for their users. For international industries based in non-English speaking countries, the professionals in charge of writing requirements are often non-native speakers of English, who rarely receive adequate training in the use of English for this task. As a result, requirements can contain a relatively large diversity of lexical and grammatical errors, which are not eliminated by the use of guidelines from controlled languages. This article investigates the distribution of errors in a corpus of requirements written in English by native speakers of French. Errors are defined on the basis of grammaticality and acceptability principles, and classified using comparable categories. Results show a high proportion of errors in the Noun Phrase, notably through modifier stacking, and errors consistent with simplification strategies. Comparisons with similar corpora in other genres reveal the specificity of the distribution of errors in requirements. This research also introduces possible applied uses, in the form of strategies for the automatic detection of errors, and in-person training provided by certification boards in requirements authoring.

Given a controversial issue, argument mining from natural language texts (news papers, and any form of text on the Internet) is extremely challenging: domain knowledge is often required together with appropriate forms of inferences to identify arguments. This contribution explores the types of knowledge that are required and how they can be paired with reasoning schemes, language processing and language resources to accurately mine arguments. We show via corpus analysis that the Generative Lexicon, enhanced in different manners and viewed as both a lexicon and a domain knowledge representation, is a relevant approach. In this paper, corpus annotation for argument mining is first developed, then we show how the generative lexicon approach must be adapted and how it can be paired with language processing patterns to extract and specify the nature of arguments. Our approach to argument mining is thus knowledge driven.

In this paper, we investigate some language acquisition facets of an auto-adaptative system that can automatically acquire most of the relevant lexical knowledge and authoring practices for an application in a given domain. This is the LELIO project: producing customized LELIE solutions. Our goal, within the framework of LELIE (a system that tags language uses that do not follow the Constrained Natural Language principles), is to automate the long, costly and error prone lexical customization of LELIE to a given application domain. Technical texts being relatively restricted in terms of syntax and lexicon, results obtained show that this approach is feasible and relatively reliable. By auto-adaptative, we mean that the system learns from a sample of the application corpus the various lexical terms and uses crucial for LELIE to work properly (e.g. verb uses, fuzzy terms, business terms, stylistic patterns). A technical writer validation method is developed at each step of the acquisition.

In this paper, we briefly present the objectives of Inference Anchoring Theory (IAT) and the formal structure which is proposed for dialogues. Then, we introduce our development corpus, and a computational model designed for the identification of discourse minimal units in the context of argumentation and the illocutionary force associated with each unit. We show the categories of resources which are needed and how they can be reused in different contexts.

In this paper, we present the foundations and the properties of the DISLOG language, a logic-based language designed to describe and implement discourse structure analysis. Dislog has the flexibility and the expressiveness of a rule-based system, it offers the possibility to include knowledge and reasoning capabilities and the expression a variety of well-formedness constraints proper to discourse. Dislog is embedded into the platform that offers an engine with various processing capabilities and a programming environment.

In this paper, we present an analysis method, a set of rules, lexical resources dedicated to discourse relation identification, in particular for explanation analysis. The following relations are described with prototypical rules: instructions, advice, warnings, illustration, restatement, purpose, condition, circumstance, concession, contrast and some forms of causes. Rules are developed for French and English. The approach used to describe the analysis of such relations is basically generative and also provides a conceptual view of explanation. The implementation is realized in Dislog, using the logic-based platform, and the Dislog language, that also allows for the integration of knowledge and reasoning into rules describing the structure of explanation.

In this paper, we present the first phase of the LELIE project. A tool that detects business errors in technical documents such as procedures or requirements is introduced. The objective is to improve readability and to check for some elements of contents so that risks that could be entailed by misunderstandings or typos can be prevented. Based on a cognitive ergonomics analysis, we survey a number of frequently encountered types of errors and show how they can be detected using the discourse analysis platform. We show how errors can be annotated, give figures on error frequencies and analyze how technical writers perceive our system.

Dans ce document, nous présentons les principales caractéristiques de <TextCoop>, un environnement basé sur les grammaires logiques dédié à l’analyse de structures discursives. Nous étudions en particulier le langage DisLog qui fixe la structure des règles et des spécifications qui les accompagnent. Nous présentons la structure du moteur de <TextCoop> en indiquant au fur et à mesure du texte l’état du travail, les performances et les orientations en particulier en matière d’environnement, d’aide à l’écriture de règles et de développement applicatif.

This paper describes an annotation scheme for argumentation in opinionated texts such as newspaper editorials, developed from a corpus of approximately 500 English texts from Nepali and international newspaper sources. We present the results of analysis and evaluation of the corpus annotation ― currently, the inter-annotator agreement kappa value being 0.80 which indicates substantial agreement between the annotators. We also discuss some of linguistic resources (key factors for distinguishing facts from opinions, opinion lexicon, intensifier lexicon, pre-modifier lexicon, modal verb lexicon, reporting verb lexicon, general opinion patterns from the corpus etc.) developed as a result of our corpus analysis, which can be used to identify an opinion or a controversial issue, arguments supporting an opinion, orientation of the supporting arguments and their strength (intrinsic, relative and in terms of persuasion). These resources form the backbone of our work especially for performing the opinion analysis in the lower levels, i.e., in the lexical and sentence levels. Finally, we shed light on the perspectives of the given work clearly outlining the challenges.

This paper presents ongoing work dedicated to parsing the textual structure of procedural texts. We propose here a model for the intructional structure and criteria to identify its main components: titles, instructions, warnings and prerequisites. The main aim of this project, besides a contribution to text processing, is to be able to answer procedural questions (How-to? questions), where the answer is a well-formed portion of a text, not a small set of words as for factoid questions.

In this paper, we present the results of a preliminary investigation that aims at constructing a repository of preposition syntactic and semantic behaviors. A preliminary frame-based format for representing their prototypical behavior is then proposed together with related inferential patterns that describe functional or paradigmatic relations between preposition senses.