Text Relatedness Based on a Word Thesaurus

Abstract

The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.

KDTA: Automated Knowledge-Driven Text Annotation

“…1(a) and 1(b) respectively). The latter is the core of the proposed method and utilizes WordNet, and Wikipedia as knowledge bases, as well as two respective measures of semantic relatedness: a dictionarybased measure, namely Omiotis [4], which is based on WordNet, and a Wikipedia-based [3]. Both measures have been shown to achieve state-of-the-art performance in measuring word-to-word semantic relatedness [4].…”

“…A Web application that implements KDTA is publicly available online 4 . Firstly, the user may upload an ontology in OWL format or may select an already existing ontology by leaving the corresponding browsing path empty.…”

A Fast Approach for Semantic Similar Short Texts Retrieval

“…The setting of the problem is that users always ask for those most semantically related to their queries from a huge text collection. A common solution is applying the state-of-the-art short texts similarity measurement techniques (Islam and Inkpen, 2008;Li et al, 2006;Mihalcea et al, 2006;Sahami and Heilman, 2006;Tsatsaronis et al, 2010;Mohler et al, 2011;, and then return the top-k ones * Corresponding author.…”

“…Moreover, we focus on the top-k issue because users commonly do not care about the individual similarity score but only the sorted results. Furthermore, most of the previous studies (Islam and Inkpen, 2008;Li et al, 2006;Tsatsaronis et al, 2010; need to set predefined threshold to filter out those dissimilar texts which is rather difficult to determine by users.…”

“…This indicates that the traditional NLP techniques for long texts may not be always appropriate to apply to short texts. The related works on short texts similarity measurement can be classified into the following major categories, i.e., (1) inner resource based strategy (Li et al, 2006;Islam and Inkpen, 2008); (2) outer resource based strategy (Tsatsaronis et al, 2010;Mihalcea et al, 2006;Islam and Inkpen, 2008;; and (3) hybrid based strategy (Islam and Inkpen, 2008;Li et al, 2006;.…”