Features of an Error Correction Memory to Enhance Technical Texts Authoring in LELIE

International audienceIn this paper, we investigate the notion of error correction memory applied to technical texts. The main purpose is to introduce flexibility and context sensitivity in the detection and the correction of errors related to Constrained Natural Language (CNL) principles. This is realized by enhancing error detection paired with relatively generic correction patterns and contextual correction recommendations. Patterns are induced from previous corrections made by technical writers for a given type of text. The impact of such an error correction memory is also investigated from the point of view of the technical writer"s cognitive activity. The notion of error correction memory is developed within the framework of the LELIE project an experiment is carried out on the case of fuzzy lexical items and negation, which are both major problems in technical writing. Language processing and knowledge representation aspects are developed together with evaluation directions

Argument Compound Mining in Technical Texts: linguistic structures, implementation and annotation schemas

International audienceIn this paper, we motivate and develop the linguistic characteristics of argument compounds. The discourse structures that refine or elaborate arguments are analysed and their cognitive impact in argumentation is developed. An implementation is then presented. It is carried out in Dislog on the TextCoop platform. Dislog allows high level specifications in logic for fast and easy prototyping at a high level of linguistic adequacy. Elements of an indicative evaluation are provided

Some Challenges of Advanced Question-Answering: an Experiment with How-to Questions

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

An exploration of the relatedness problem between arguments: combining the generative lexicon with inference

International audienceGiven a controversial issue, argument mining from natural language texts is extremely challenging: domain knowledge is often required together with appropriate forms of inferences. This contribution explores the use of the Generative Lexicon viewed as both a lexicon and a domain knowledge representation

Discourse structure analysis for requirement mining

International audienceIn this work, we first introduce two main approaches to writing requirements and then propose a method based on Natural Language Processing to improve requirement authoring and the overall coherence, cohesion and organization of requirement documents. We investigate the structure of requirement kernels, and then the discourse structure associated with those kernels. This will then enable the system to accurately extract requirements and their related contexts from texts (called requirement mining). Finally, we relate a first experimentation on requirement mining based on texts from seven companies. An evaluation that compares those results with manually annotated corpora of documents is given to conclude

Construction de réponses coopératives : du corpus à la modélisation informatique

Les stratégies utilisées pour la recherche d’information dans le cadre du Web diffèrent d’un moteur de recherche à un autre, mais en général, les résultats obtenus ne répondent pas directement et simplement à la question posée. Nous présentons une stratégie qui vise à définir les fondements linguistiques et de communication d’un système d’interrogation du Web qui soit coopératif avec l’usager et qui tente de lui fournir la réponse la plus appropriée possible dans sa forme et dans son contenu. Nous avons constitué et analysé un corpus de questions-réponses coopératives construites à partir des sections Foire Aux Questions (FAQ) de différents services Web aux usagers. Cela constitue à notre sens une bonne expérimentation de ce que pourrait être une communication directe en langue naturelle sur le Web. Cette analyse de corpus a permis d’extraire les caractéristiques majeures du comportement coopératif et de construire l’architecture de notre système informatique webcoop, que nous présentons à la fin de cet article.Algorithms and strategies used on the Web for information retrieval differ from one search engine to another, but, in general, results do not lead to very accurate and informative answers. In this paper, we describe our strategy for designing a cooperative question answering system that aims at producing the most appropriate answers to natural language questions. To characterize these answers, we collected a corpus of cooperative question in our opinion answer pairs extracted from Frequently Asked Questions. The analysis of this corpus constitutes a good experiment on what a cooperative natural language communication on the Web could be. This analysis allows for the elaboration of a general architecture for our cooperative question answering system webcoop, which we present at the end of this paper

LELIE - An Intelligent Assistant for Improving Requirement Authoring

International audienceWhen writing or revising a set of requirements, or any technical document, it is particularly challenging to make sure that texts read easily and are unambiguous for any domain actor. Experience shows that even with several levels of proofreading and validation, most texts still contain a large number of language errors (lexical, grammatical, style, business, w.r.t. authoring recommendations), and lack of overall cohesion and coherence. LELIE [a] has been designed to track these errors and, whenever possible, to suggest corrections. LELIE has obviously an impact on the technical writer behavior: LELIE rapidly becomes an essential and user-friendly authoring companion

Constraints on long distance dependencies in gapping grammars

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Challenges of argument mining: generating an argument synthesis based on the Qualia structure

Given a controversial issue, argument mining from texts in natural language is extremely challenging: besides linguistic aspects, domain knowledge is often required together with appropriate forms of inferences to identify arguments. A major challenge is then to organize the arguments which have been mined to generate a synthesis that is relevant and usable. We show that the Generative Lexicon (GL) Qualia structure, enhanced in different manners and associated with inferences and language patterns, allows to capture the typical concepts found in arguments and to organize a relevant synthesis