Analyse des performances de modèles de langage sub-lexicale pour des langues peu-dotées à morphologie riche (Performance analysis of sub-word language modeling for under-resourced languages with rich morphology: case study on Swahili and Amharic) [in French]

Analyse des performances de modèles de langage sub-lexicale pour des langues peu-dotées à morphologie riche

Performance analysis of sub-word language modeling for under-resourced languages with rich morphology : case study on Swahili and Amharic This paper investigates the impact on ASR performance of sub-word units for two underresourced african languages with rich morphology (Amharic and Swahili). Two subword units are considered : syllable and morpheme, the latter being obtained in a supervised or...

A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages

______________________________________________________________________________________________ Word Sense Disambiguation (WSD), the process of automatically identifying the meaning of a polysemous word in a sentence, is a fundamental task in Natural Language Processing (NLP). Progress in this approach to WSD opens up many promising developments in the field of NLP and its applications. Indeed, ...

External Lexical Information for Multilingual Part-of-Speech Tagging

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. Here we compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similar...

A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages (État de l'art de l'induction de sens: une voie vers la désambiguïsation lexicale pour les langues peu dotées) [in French]

Is Bad Structure Better Than No Structure?: Unsupervised Parsing for Realisation Ranking

In natural language generation using symbolic grammars, state-of-the-art realisation rankers use statistical models incorporating both language model and structural features. The rankers depend on multiple structures produced by the particular large-scale symbolic grammars to rank the output; for languages with smaller resources and in-development grammars, we look at the feasibility of an alte...