ADW, free software to measure semantic similarity
ADW is a software for measuring semantic similarity of arbitrary pairs of lexical items, from word senses to texts, based on "Align, Disambiguate, and Walk", a WordNet-based state-of-the-art semantic similarity approach. Get it on github.
By Taher Pilehvar (U. Roma), Oct 2014.
Align, Disambiguate, and Walk (ADW)
Semantic similarity of arbitrary pairs of lexical items, from word senses to texts!
The Linguistic Computing Laboratory of the Sapienza University of Rome is pleased to announce the first release of ADW.
ADW is a software for measuring semantic similarity of arbitrary pairs of lexical items, from word senses to texts. The software is based on "Align, Disambiguate, and Walk" [1], a WordNet-based state-of-the-art semantic similarity approach presented in ACL 2013.
Features in ADW:
To obtain the software, please visit ADW's github repository at:
https://github.com/pilehvar/ADW
You can also try ADW's online demo at:
lcl.uniroma1.it/adw/
Reference
[1] M. T. Pilehvar, D. Jurgens and R. Navigli. Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity. Proc. of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, Bulgaria, August 4-9, 2013, pp. 1341-1351.
Gregory Piatetsky: I tried the online demo, and the semantic similarity of
so the computed values have to be used for relative comparison, not in an absolute sense.
Related:
Align, Disambiguate, and Walk (ADW)
Semantic similarity of arbitrary pairs of lexical items, from word senses to texts!
The Linguistic Computing Laboratory of the Sapienza University of Rome is pleased to announce the first release of ADW.
ADW is a software for measuring semantic similarity of arbitrary pairs of lexical items, from word senses to texts. The software is based on "Align, Disambiguate, and Walk" [1], a WordNet-based state-of-the-art semantic similarity approach presented in ACL 2013.
Features in ADW:
- State-of-the-art performance at multiple lexical levels [1] (word similarity on data-sets such as RG-65 and TOEFL, sentence similarity - the STS task, and WordNet sense clustering).
- Cross-level semantic similarity, i.e., between different types of lexical items (e.g., a word sense and a phrase).
- Available via easy-to-use Java APIs.
- Off-the-shelf: no need for any training or tuning of parameters.
- Suitable for upcoming SemEval-2015 Tasks 1, 2, and 3
To obtain the software, please visit ADW's github repository at:
https://github.com/pilehvar/ADW
You can also try ADW's online demo at:
lcl.uniroma1.it/adw/
Reference
[1] M. T. Pilehvar, D. Jurgens and R. Navigli. Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity. Proc. of the 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Sofia, Bulgaria, August 4-9, 2013, pp. 1341-1351.
Gregory Piatetsky: I tried the online demo, and the semantic similarity of
- "apple" and "orange" is 0.47,
- "tangerine" and "orange" is 0.78,
- "shoe" and "orange" is 0.33
so the computed values have to be used for relative comparison, not in an absolute sense.
Related: