This work introduces a new unsupervised approach to multilingual word sense disambiguation. Its main purpose is to automatically choose the intended sense (meaning) of a word in a particular context for different languages. It does so by selecting the correct Babel synset for the word and the various Wiki Page titles that mention the word. BabelNet contains all the output information that our s...

This paper explores an automatic WordNet synset assignment to the bi-lingual dictionaries of languages having limited lexicon information. Generally, a term in a bilingual dictionary is provided with very limited information such as part-of-speech, a set of synonyms, and a set of English equivalents. This type of dictionary is comparatively reliable and can be found in an electronic form from v...

Recently, two different entropy measures based on the relational structure of English compounds have been used in studies of semantic transparency and lexical decision times. Pham & Baayen (2013) show that the entropy of semantic relations in the modifier family is negatively correlated with semantic transparency. Schmidtke et al. (2015) find that the relational entropy for individual compounds...

In order to deal with ambiguity in natural language, it is common to organise words, according to their senses, in synsets, which are groups of synonymous words that can be seen as concepts. The manual creation of a broad-coverage synset base is a timeconsuming task, so we take advantage of dictionary definitions for extracting synonymy pairs and clustering for identifying synsets. Since word s...

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 incom...

Word Sense Disambiguation (WSD) is one of the open problems in the area of natural language processing. Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context. It has been observed that such approaches often find it difficult to beat the WordNet First Sense (WFS) baseline which assigns the sens...

The IndoWordNet 1 Consortium consists of member institutions developing WordNet using the expansion approach. The WordNets developed using expansion approach are very much influenced by the source language and may not reflect the richness of the target language (Walawalikar et al., 2010). And therefore the IndoWordNet Community decided to develop concepts which were specific to their respective...

One of the widely used approaches to Sentiment Analysis (SA) is lexicon-based approach that depends on sentiment-annotated lexical resources (such as SentiWordNet (SWN)). A broad variety of such resources are Synsetbased Lexical Databases (SLDs) (e.g. SWN is based on WordNet (WN)) and represent sentiment degrees of synonym groups of LDs, called “synsets.” However, synsets themselves were open t...

This work presents an unsupervised approach for improving WordNet that builds upon recent advances in document and sense representation via distributional semantics. We apply our methods to construct Wordnets in French and Russian, languages which both lack good manual constructions.1 These are evaluated on two new 600-word test sets for word-to-synset matching and found to improve greatly upon...

In this work lexical choice in generation for Machine Translation is explored using lexical semantics. We address this problem by replacing lemmas with synonyms in the abstract representations that are used as input for generation, given a WordNet synset. In order to find the correct lemma for each node we propose to map dependency trees to Hidden Markov Trees that describe the probability of a...