Franck Sajous

Wiktionary and NLP: improving synonymy networks

Wiktionary, a satellite of the Wikipedia initiative, can be seen as a potential resource for Natural Language Processing. It requires however to be processed before being used efficiently as an NLP resource. After describing the relevant aspects of Wiktionary for our purposes, we focus on its structural properties. Then, we describe how we extracted synonymy networks from this resource. We provide an in-depth study of these synonymy networks and compare them to those extracted from traditional resources. Finally, we describe two methods for semi-automatically improving this network by adding missing relations: (i) using a kind of semantic proximity measure; (ii) using translation relations of Wiktionary itself.

Semi-automatic endogenous enrichment of collaboratively constructed lexical resources: piggybacking onto wiktionary

The lack of large-scale, freely available and durable lexical resources, and the consequences for NLP, is widely acknowledged but the attempts to cope with usual bottlenecks preventing their development often result in dead-ends. This article introduces a language-independent, semi-automatic and endogenous method for enriching lexical resources, based on collaborative editing and random walks through existing lexical relationships, and shows how this approach enables us to overcome recurrent impediments. It compares the impact of using different data sources and similarity measures on the task of improving synonymy networks. Finally, it defines an architecture for applying the presented method to Wiktionary and explains how it has been implemented.

Semi-automatic enrichment of crowdsourced synonymy networks: the WISIGOTH system applied to Wiktionary

Semantic lexical resources are a mainstay of various Natural Language Processing applications. However, comprehensive and reliable resources are rare and not often freely available. Handcrafted resources are too costly for being a general solution while automatically-built resources need to be validated by experts or at least thoroughly evaluated. We propose in this paper a picture of the current situation with regard to lexical resources, their building and their evaluation. We give an in-depth description of Wiktionary, a freely available and collaboratively built multilingual dictionary. Wiktionary is presented here as a promising raw resource for NLP. We propose a semi-automatic approach based on random walks for enriching Wiktionary synonymy network that uses both endogenous and exogenous data. We take advantage of the wiki infrastructure to propose a validation “by crowds”. Finally, we present an implementation called WISIGOTH, which supports our approach.

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

We 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.