Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1613/jair.2880 | Journal of Artificial Intelligence Research |
Keywords | DocType | Volume |
word-to-word semantic relatedness measure,lexicon-based method,new measure,implicit semantic link,text relatedness,word thesaurus,text retrieval,text-to-text semantic relatedness,semantic relatedness measure,semantic relatedness,text segmentation | Journal | abs/1401.5699 |
Issue | ISSN | Citations |
1 | Journal Of Artificial Intelligence Research, Volume 37, pages
1-39, 2010 | 69 |
PageRank | References | Authors |
1.96 | 70 | 31 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Tsatsaronis | 1 | 427 | 29.66 |
| Iraklis Varlamis | 2 | 503 | 52.08 |
| Michalis Vazirgiannis | 3 | 3942 | 268.00 |
| Uzi Zahavi | 4 | 246 | 11.89 |
| Ariel Felner | 5 | 1239 | 105.75 |
| Neil Burch | 6 | 373 | 29.51 |
| Robert C. Holte | 7 | 3041 | 414.38 |
| Shahar Dobzinski | 8 | 889 | 59.86 |
| Noam Nisan | 9 | 8170 | 809.08 |
| Henrik Reif Andersen | 10 | 465 | 33.39 |
| Tarik Hadzic | 11 | 188 | 13.18 |
| d pisinger | 12 | 69 | 1.96 |
| Peter D. Turney | 13 | 6084 | 534.36 |
| Patrick Pantel | 14 | 3980 | 232.69 |
| i j varzinczak | 15 | 69 | 1.96 |
| Shaolin Qu | 16 | 126 | 7.00 |
| Joyce Yue Chai | 17 | 985 | 70.50 |
| Robert Mateescu | 18 | 193 | 12.42 |
| Kalev Kask | 19 | 292 | 21.35 |
| Vibhav Gogate | 20 | 563 | 44.67 |
| Rina Dechter | 21 | 5387 | 703.16 |
| raghav aras | 22 | 69 | 1.96 |
| alain dutech | 23 | 69 | 1.96 |
| david l chen | 24 | 69 | 1.96 |
| Joohyun Kim | 25 | 292 | 22.75 |
| Raymond J. Mooney | 26 | 10408 | 961.10 |
| w van der hoek | 27 | 69 | 1.96 |
| dirk walther | 28 | 81 | 2.47 |
| Michael P. Wellman | 29 | 4715 | 757.80 |
| yagil engel | 30 | 69 | 1.96 |
| michael p wellman | 31 | 69 | 1.96 |