Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Grouping Synonyms by Definitions

We present a method for grouping the synonyms of a lemma according to its dictionary senses. The senses are defined by a large machine readable dictionary for French, the TLFi (Tr\'esor de la langue fran\c{c}aise informatis\'e) and the synonyms are given by 5 synonym dictionaries (also for French). To evaluate the proposed method, we manually constructed a gold standard where for each (word, definition) pair and given the set of synonyms defined for that word by the 5 synonym dictionaries, 4 lexicographers specified the set of synonyms they judge adequate. While inter-annotator agreement ranges on that task from 67% to at best 88% depending on the annotator pair and on the synonym dictionary being considered, the automatic procedure we propose scores a precision of 67% and a recall of 71%. The proposed method is compared with related work namely, word sense disambiguation, synonym lexicon acquisition and WordNet construction

Acquisition and enrichment of morphological and morphosemantic knowledge from the French Wiktionary

International audienceWe present two approaches to automatically acquire morphologically related words from Wiktionary. Starting with related words explicitly mentioned in the dictionary, we propose a method based on orthographic similarity to detect new derived words from the entries' definitions with an overall accuracy of 93.5%. Using word pairs from the initial lexicon as patterns of formal analogies to filter new derived words enables us to rise the accuracy up to 99%, while extending the lexicon's size by 56%. In a last experiment, we show that it is possible to semantically type the morphological definitions, focusing on the detection of process nominals

GLAWI, a free XML-encoded Machine-Readable Dictionary built from the French Wiktionary

International audienceThis article introduces GLAWI, a large XML-encoded machine-readable dictionary automatically extracted from Wiktionnaire, the French edition of Wiktionary. GLAWI contains 1,341,410 articles and is released under a free license. Besides the size of its headword list, GLAWI inherits from Wiktionnaire its original macrostructure and the richness of its lexicographic descriptions: articles contain etymologies, definitions, usage examples, inflectional paradigms, lexical relations and phonemic transcriptions. The paper first gives some insights on the nature and content of Wiktionnaire, with a particular focus on its encoding format, before presenting our approach, the standardization of its microstructure and the conversion into XML. First intended to meet NLP needs, GLAWI has been used to create a number of customized lexicons dedicated to specific uses including linguistic description and psycholinguistics. The main one is GLÀFF, a large inflectional and phonological lexicon of French. We show that many more specific on demand lexicons can be easily derived from the large body of lexical knowledge encoded in GLAWI