Could we automatically reproduce semantic relations of an information retrieval thesaurus?

A well constructed thesaurus is recognized as a valuable source of semantic information for various applications, especially for Information Retrieval. The main hindrances to using thesaurus-oriented approaches are the high complexity and cost of manual thesauri creation. This paper addresses the problem of automatic thesaurus construction, namely we study the quality of automatically extracted semantic relations as compared with the semantic relations of a manually crafted thesaurus. The vector-space model based on syntactic contexts was used to reproduce relations between the terms of a manually constructed thesaurus. We propose a simple algorithm for representing both single word and multiword terms in the distributional space of syntactic contexts. Furthermore, we propose a method for evaluation quality of the extracted relations. Our experiments show significant difference between the automatically and manually constructed relations: while many of the automatically generated relations are relevant, just a small part of them could be found in the original thesaurus

FreDist: Automatic construction of distributional thesauri for French

International audienceIn this article we present FreDist, a freely available software package for the automatic construction of distributional thesauri from text corpora, as well as an evaluation of various distributional similarity metrics for French. Following from the work of Lin (1998) and Curran (2004), we use a large corpus of journalistic text and implement different choices for the type of lexical context relation, the weight function, and the measure function needed to build a distributional thesaurus. Using the EuroWordNet and \wolf wordnet resources for French as gold-standard references for our evaluation, we obtain the novel result that combining bigram and syntactic dependency context relations results in higher quality distributional thesauri. In addition, we hope that our software package and a joint release of our best thesauri for French will be useful to the NLP community

Automatic thesaurus construction

Sydney, NS

Fighting with the Sparsity of Synonymy Dictionaries

Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the structure of the input synonymy graph: sparseness of the input dictionary can substantially reduce the quality of the extracted synsets. In this paper, we propose two different approaches designed to alleviate the incompleteness of the input dictionaries. The first one performs a pre-processing of the graph by adding missing edges, while the second one performs a post-processing by merging similar synset clusters. We evaluate these approaches on two datasets for the Russian language and discuss their impact on the performance of synset induction methods. Finally, we perform an extensive error analysis of each approach and discuss prominent alternative methods for coping with the problem of the sparsity of the synonymy dictionaries.Comment: In Proceedings of the 6th Conference on Analysis of Images, Social Networks, and Texts (AIST'2017): Springer Lecture Notes in Computer Science (LNCS

A Unified multilingual semantic representation of concepts

Semantic representation lies at the core of several applications in Natural Language Processing. However, most existing semantic representation techniques cannot be used effectively for the representation of individual word senses. We put forward a novel multilingual concept representation, called MUFFIN , which not only enables accurate representation of word senses in different languages, but also provides multiple advantages over existing approaches. MUFFIN represents a given concept in a unified semantic space irrespective of the language of interest, enabling cross-lingual comparison of different concepts. We evaluate our approach in two different evaluation benchmarks, semantic similarity and Word Sense Disambiguation, reporting state-of-the-art performance on several standard datasets