Pascal Vaillant

Proper names extraction from fax images combining textual and image features

In the frame of a unified messaging system, a crucial task of the system is to provide the user with key information on every message received, like keywords reflecting the object of the message, or the name of the sender. However, in the case of facsimiles, this information is not as easy to detect as in the case of e-mails, since no standard headers are defined. The aim of the presented work is to identify and extract specific information (the name of the sender) from a fax cover page. For this purpose, methods based on image document analysis (OCR recognition, physical blocks selection), and text analysis methods (optimized dictionary lookup, local grammar rules), are implemented to work in parallel. The fusion of their results brings a more accurate guess than any of the methods would achieve separately.

Automatic name extraction from degraded document images

The problem addressed in this paper is the automatic extraction of names from a document image. Our approach relies on the combination of two complementary analyses. First, the image-based analysis exploits visual clues to select the regions of interest in the document. Second, the textual-based analysis searches for name patterns and low-level word textual features. Both analyses are then combined at the word level through a neural network fusion scheme. Reported results on degraded documents such as facsimile and photocopied technical journals demonstrate the interest of the combined approach.

A Semantics-Based Communication System for Dysphasic Subjects

Dysphasic subjects do not have complete linguistic abilities and only produce a weakly structured, topicalized language. They are offered artificial symbolic languages to help them communicate in a way more adapted to their linguistic abilities. After a structural analysis of a corpus of utterances from children with cerebral palsy, we define a semantic lexicon for such a symbolic language. We use it as the basis of a semantic analysis process able to retrieve an interpretation of the utterances. This semantic analyser is currently used in an application designed to convert iconic languages into natural language; it might find other uses in the field of language rehabilitation.

Soft memberships for spectral clustering, with application to permeable language distinction

Recently, a large amount of work has been devoted to the study of spectral clustering—a powerful unsupervised classification method. This paper brings contributions to both its foundations, and its applications to text classification. Departing from the mainstream, concerned with hard membership, we study the extension of spectral clustering to soft membership (probabilistic, EM style) assignments. One of its key features is to avoid the complexity gap of hard membership. We apply this theory to a challenging problem, text clustering for languages having permeable borders, via a novel construction of Markov chains from corpora. Experiments with a readily available code clearly display the potential of the method, which brings a visually appealing soft distinction of languages that may define altogether a whole corpus.

Interpretation of iconic utterances based on contents representation: Semantic analysis in the PVI system

This article focuses on the need for technological aid for agrammatics, and presents a system designed to meet this need. The field of Augmentative and Alternative Communication (AAC) explores ways to allow people with speech or language disabilities to communicate. The use of computers and natural language processing techniques offers a range of new possibilities in this direction. Yet AAC addresses speech deficits mainly, not linguistic disabilities. A model of aided AAC interfaces with a place for natural language processing is presented. The PVI system, described in this contribution, makes use of such advanced techniques. It has been developed at Thomson-CSF for the use of children with cerebral palsy. It presents a customizable interface helping the disabled to compose sequences of icons displayed on a computer screen. A semantic parser, using lexical semantics information, is used to determine the best case assignments for predicative icons in the sequence. It maximizes a global value, the ‘semantic harmony’ of the sequence. The resulting conceptual graph is fed to a natural language generation module which uses Tree Adjoining Grammars (TAG) to generate French sentences. Evaluation by users demonstrates the systems strengths and limitations, and shows the ways for future developments.

Using Graph Transformation Algorithms to Generate Natural Language Equivalents of Icons Expressing Medical Concepts

A graphical language addresses the need to communicate medical information in a synthetic way. Medical concepts are expressed by icons conveying fast visual information about patients’ current state or about the known effects of drugs. In order to increase the visual language’s acceptance and usability, a natural language generation interface is currently developed. In this context, this paper describes the use of an informatics method – graph transformation – to prepare data consisting of concepts in an OWL-DL ontology for use in a natural language generation component. The OWL concept may be considered as a star-shaped graph with a central node. The method transforms it into a graph representing the deep semantic structure of a natural language phrase. This work may be of future use in other contexts where ontology concepts have to be mapped to half-formalized natural language expressions.

Modelling Semantic Association and Conceptual Inheritance for Semantic Analysis

Allowing users to interact through language borders is an interesting challenge for information technology. For the purpose of a computer assisted language learning system, we have chosen icons for representing meaning on the input interface, since icons do not depend on a particular language. However, a key limitation of this type of communication is the expression of articulated ideas instead of isolated concepts. We propose a method to interpret sequences of icons as complex messages by reconstructing the relations between concepts, so as to build conceptual graphs able to represent meaning and to be used for natural language sentence generation. This method is based on an electronic dictionary containing semantic information.