Semantic similarity knowledge and its applications

Current Issues in Linguistic Theory, 2004

We have implemented a system that measures semantic similarity using a computerized 1987 Roget's Thesaurus, and evaluated it by performing a few typical tests. We compare the results of these tests with those produced by WordNet-based similarity measures. One of the benchmarks is Miller and Charles' list of 30 noun pairs to which human judges had assigned similarity measures. We correlate these measures with those computed by several NLP systems. The 30 pairs can be traced back to Rubenstein and Goodenough's 65 pairs, which we have also studied. Our Roget's-based system gets correlations of .878 for the smaller and .818 for the larger list of noun pairs; this is quite close to the .885 that Resnik obtained when he employed humans to replicate the Miller and Charles experiment. We further evaluate our measure by using Roget's and WordNet to answer 80 TOEFL, 50 ESL and 300 Reader's Digest questions: the correct synonym must be selected amongst a group of four words. Our system gets 78.75%, 82.00% and 74.33% of the questions respectively.

Lecture Notes in Computer Science, 2011

Despite the growth in digitization of data, there are still many languages without sufficient corpora to achieve valid measures of semantic similarity. If it could be shown that manually-assigned similarity scores from one language can be transferred to another language, then semantic similarity values could be used for languages with fewer resources. We test an automatic word similarity measure based on second-order co-occurrences in the Google ngram corpus, for English, German, and French. We show that the scores manually-assigned in the experiments of Rubenstein and Goodenough's for 65 English word pairs can be transferred directly into German and French. We do this by conducting human evaluation experiments for French word pairs (and by using similarly produced scores for German). We show that the correlation between the automatically-assigned semantic similarity scores and the scores assigned by human evaluators is not very different when using the Rubenstein and Goodenough's scores across language, compared to the language-specific scores.

2016

Semantic similarity and relatedness measures play an important role in natural language processing applications. In this paper, we present the IndoWordNet::Similarity tool and interface, designed for computing the semantic similarity and relatedness between two words in IndoWordNet. A java based tool and a web interface have been developed to compute this semantic similarity and relatedness. Also, Java API has been developed for this purpose. This tool, web interface and the API are made available for the research purpose.

Lecture Notes in Computer Science, 2014

The issue of sentence semantic similarity is important and essential to many applications of Natural Language Processing. This issue was treated in some frameworks dealing with the similarity between short texts especially with the similarity between sentence pairs. However, the semantic component was paradoxically weak in the proposed methods. In order to address this weakness, we propose in this paper a new method to estimate the semantic sentence similarity based on the LMF ISO-24613 standard. Indeed, LMF provides a fine structure and incorporates an abundance of lexical knowledge which is interconnected together, notably sense knowledge such as semantic predicates, semantic classes, thematic roles and various sense relations. Our method proved to be effective through the applications carried out on the Arabic language. The main reason behind this choice is that an Arabic dictionary which conforms to the LMF standard is at hand within our research team. Experiments on a set of selected sentence pairs demonstrate that the proposed method provides a similarity measure that coincides with human intuition.

Journal of Artificial Intelligence Research, 2010

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.

2001

This paper explores different strategies for extracting similarity relations between words from parsed text corpora. The strategies we have analysed do not require supervised training nor semantic information available from general lexical resources. They differ in the amount and the quality of the syntactic contexts used to compare words. The paper presents in details the notion of syntactic context and how syntactic information could be used to extract semantic regularities of word sequences. Finally, experimental tests with a Brazilian Portuguese corpus demonstrate that similarity measures based on fine-grained and elaborate syntactic contexts perform better than those based on poorly defined contexts. David Faure and Claire N'edellec. 1998. ASIUM: Learning subcategorization frames and restrictions of selection. 10th European Conference on Machine Learning, ECML98. Workshop on Text Mining. David Yarowsky. 1992. Word-sense disambiguation using statistical models of Roget's categories trained on large corpora.

Modeling, Simulation and Optimization of Complex Processes, 2008

We present a new information theoretic approach to measure the semantic similarity between concepts. By exploiting advantages of distance (edge-base) approach for taxonomic tree-like concepts, we enhance the strength of information theoretic (node-based) approach. Our measure therefore gives a complete view of word similarity, which cannot be achieved by solely applying node-based approaches. Our experimental measure achieves 88%, correlating with human rating.

2006

This paper presents a new corpus-based method for calculating the semantic similarity of two target words. Our method, called Second Order Co-occurrence PMI (SOC-PMI), uses Pointwise Mutual Information to sort lists of important neighbor words of the two target words. Then we consider the words which are common in both lists and aggregate their PMI values (from the opposite list) to calculate the relative semantic similarity. Our method was empirically evaluated using Miller and Charler's (1991) 30 noun pair subset, 65 noun pairs, 80 synonym test questions from the Test of English as a Foreign Language (TOEFL), and 50 synonym test questions from a collection of English as a Second Language (ESL) tests. Evaluation results show that our method outperforms several competing corpus-based methods.

IEEE Transactions on Knowledge and Data Engineering, 2003

... 3. Implementation. Back to Top. Two databases are used in the implementation of the proposed method of semantic similarity; they are WordNet [ 15 ] and the Brown Corpus [ 7 ]. Both databases are publicly available and widely used in previously published works. ...

Computational Linguistics, 2006

The quantification of lexical semantic relatedness has many applications in NLP, and many different measures have been proposed. We evaluate five of these measures, all of which use WordNet as their central resource, by comparing their performance in detecting and correcting real-word spelling errors. An information-content-based measure proposed by Jiang and Conrath is found superior to those proposed by Hirst and St-Onge, Leacock and Chodorow, Lin, and Resnik. In addition, we explain why distributional similarity is not an adequate proxy for lexical semantic relatedness.