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.

本文提出了一种基于词库的、测量文本语义相似性的新方法，称为 Omiotis，它利用了单词之间的语义相关性，将其扩展到文本之间的相关性测量，并在句子相似性和释义识别任务中进行了实验验证，结果显示该方法在选定任务及数据集上表现优异，且与基于语料库和混合方法相媲美，优于所有基于词库的语义相似性方法。