A Methodology for Identifying Terms and Patterns Specific to Requirements as a Textual Genre Using Automated Tools

International audienceAs a step in a project whose final goal is to propose a Controlled Natural Language for requirements writing at CNES (Centre National d'Études Spatiales), we intend to build the grammar of the textual genre of the requirements. One of the main issues faced when analyzing our corpus is the (sometimes subtle) difference between the terms and syntactic structures pertaining to the genre and those linked to the domain (in our case, the development of space systems) – a difference that is generally not taken into account by automated tools. In this paper, we present a methodology aimed at detecting candidate terms and textual patterns specific to the genre by combining results obtained from a terminology extractor and a data mining tool with a validated resource in use for indexing documents at CNES. The results are then illustrated by a selection of examples from our corpus

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Towards the creation of a CNL adapted to requirements writing by combining writing recommendations and spontaneous regularities : example in a Space Project

International audienceThe Quality Department of the French National Space Agency (CNES, Centre National d’Études Spatiales) wishes to design a writing guide based on the real and regular writing of requirements. As a first step in this project, the present article proposes a linguistic analysis of requirements written in French by CNES engineers. One of our goals is to determine to what extent they conform to several rules laid down in two existing Controlled Natural Languages (CNLs), namely the Simplified Technical English developed by the AeroSpace and Defense Industries Association of Europe and the Guide for Writing Requirements proposed by the International Council on Systems Engineering. Indeed, although CNES engineers are not obliged to follow any controlled language in their writing of requirements, we believe that language regularities are likely to emerge from this task, mainly due to the writers’ experience. We are seeking to identify these regularities in order to use them as a basis for a new CNL for the writing of requirements. The issue is approached using natural language processing tools to identify sentences that do not comply with the rules or contain specific linguistic phenomena. We further review these sentences to understand why the recommendations cannot (or should not) always be applied when specifying large-scale projects

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Automated analysis of Learner\u27s Research Article writing and feedback generation through Machine Learning and Natural Language Processing

Teaching academic writing in English to native and non-native speakers is a challenging task. Quite a variety of computer-aided instruction tools have arisen in the form of Automated Writing Evaluation (AWE) systems to help students in this regard. This thesis describes my contribution towards the implementation of the Research Writing Tutor (RWT), an AWE tool that aids students with academic research writing by analyzing a learner\u27s text at the discourse level. It offers tailored feedback after analysis based on discipline-aware corpora. At the core of RWT lie two different computational models built using machine learning algorithms to identify the rhetorical structure of a text. RWT extends previous research on a similar AWE tool, the Intelligent Academic Discourse Evaluator (IADE) (Cotos, 2010), designed to analyze articles at the move level of discourse. As a result of the present research, RWT analyzes further at the level of discourse steps, which are the granular communicative functions that constitute a particular move. Based on features extracted from a corpus of expert-annotated research article introductions, the learning algorithm classifies each sentence of a document with a particular rhetorical move and a step. Currently, RWT analyzes the introduction section of a research article, but this work generalizes to handle the other sections of an article, including Methods, Results and Discussion/Conclusion. This research describes RWT\u27s unique software architecture for analyzing academic writing. This architecture consists of a database schema, a specific choice of classification features, our computational model training procedure, our approach to testing for performance evaluation, and finally the method of applying the models to a learner\u27s writing sample. Experiments were done on the annotated corpus data to study the relation among the features and the rhetorical structure within the documents. Finally, I report the performance measures of our 23 computational models and their capability to identify rhetorical structure on user submitted writing. The final move classifier was trained using a total of 5828 unigrams and 11630 trigrams and performed at a maximum accuracy of 72.65%. Similarly, the step classifier was trained using a total of 27689 unigrams and 27160 trigrams and performed at a maximum accuracy of 72.01%. The revised architecture presented also led to increased speed of both training (a 9x speedup) and real-time performance (a 2x speedup). These performance rates are sufficient for satisfactory usage of RWT in the classroom. The overall goal of RWT is to empower students to write better by helping them consider writing as a series of rhetorical strategies to convey a functional meaning. This research will enable RWT to be deployed broadly into a wider spectrum of classrooms