text: This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.
  @cite_31: A new metaphor of two-dimensional text for data-driven semantic modeling of natural language is proposed, which provides an entirely new angle on the representation of text: not only syntagmatic relations are annotated in the text, but also paradigmatic relations are made explicit by generating lexical expansions. We operationalize distributional similarity in a general framework for large corpora, and describe a new method to generate similar terms in context. Our evaluation shows that distributional similarity is able to produce highquality lexical resources in an unsupervised and knowledge-free way, and that our highly scalable similarity measure yields better scores in a WordNet-based evaluation than previous measures for very large corpora. Evaluating on a lexical substitution task, we find that our contextualization method improves over a non-contextualized baseline across all parts of speech, and we show how the metaphor can be applied successfully to part-of-speech tagging. A number of ways to extend and improve the contextualization method within our framework are discussed. As opposed to comparable approaches, our framework defines a model of lexical expansions in context that can generate the expansions as opposed to ranking a given list, and thus does not require existing lexical-semantic resources.
  @cite_14: Unsupervised methods of semantic relations extraction rely on a similarity measure between lexical units. Similarity measures differ both in kinds of information they use and in the ways how this information is transformed into a similarity score. This paper is making a step further in the evaluation of the available similarity measures within the context of semantic relation extraction. We compare 21 baseline measures -- 8 knowledge-based, 4 corpus-based, and 9 web-based metrics with the BLESS dataset. Our results show that existing similarity measures provide significantly different results, both in general performances and in relation distributions. We conclude that the results suggest developing a combined similarity measure.
  @cite_3: Vector-based models of lexical semantics retrieve semantically related words automatically from large corpora by exploiting the property that words with a similar meaning tend to occur in similar contexts. Despite their increasing popularity, it is unclear which kind of semantic similarity they actually capture and for which kind of words. In this paper, we use three vector-based models to retrieve semantically related words for a set of Dutch nouns and we analyse whether three linguistic properties of the nouns influence the results. In particular, we compare results from a dependency-based model with those from a 1st and 2nd order bag-of-words model and we examine the effect of the nouns’ frequency, semantic speficity and semantic class. We find that all three models find more synonyms for high-frequency nouns and those belonging to abstract semantic classses. Semantic specificty does not have a clear influence.
  @cite_15: 
  @cite_25: Web search result clustering aims to facilitate information search on the Web. Rather than the results of a query being presented as a flat list, they are grouped on the basis of their similarity and subsequently shown to the user as a list of clusters. Each cluster is intended to represent a different meaning of the input query, thus taking into account the lexical ambiguity (i.e., polysemy) issue. Existing Web clustering methods typically rely on some shallow notion of textual similarity between search result snippets, however. As a result, text snippets with no word in common tend to be clustered separately even if they share the same meaning, whereas snippets with words in common may be grouped together even if they refer to different meanings of the input query.In this article we present a novel approach to Web search result clustering based on the automatic discovery of word senses from raw text, a task referred to as Word Sense Induction. Key to our approach is to first acquire the various senses (...
summary: Such approaches are able to discover homonymous senses of words, e.g., bank'' as slope versus bank'' as organisation @cite_25 . However, as the graphs are usually composed of semantically related words obtained using distributional methods @cite_15 @cite_31 , the resulting clusters by no means can be considered synsets. Namely, (1) they contain words related not only via synonymy relation, but via a mixture of relations such as synonymy, hypernymy, co-hyponymy, antonymy, etc. @cite_3 @cite_14 ; (2) clusters are not unique, i.e., one word can occur in clusters of different ego networks referring to the same sense, while in WordNet a word sense occurs only in a single synset.