Abstract
The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained when comparing text to process the similarity. The methodology combines semantic term similarities with a vector similarity method used within statistical analysis. A modification of the term vectors using synset similarity values addresses issues that are encountered with sparse text. LSS is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.
Original language | English |
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Title of host publication | 13th UK Workshop on Computational Intelligence (UKCI), 2013 |
Publisher | IEEE |
Pages | 221-227 |
Number of pages | 7 |
ISBN (Print) | 9781479915682 |
DOIs | |
Publication status | Published - 2013 |
Event | 13th UK Workshop on Computational Intelligence (UKCI) 2013 - University of Surrey, Guildford, United Kingdom Duration: 9 Sept 2013 → 11 Sept 2013 Conference number: 13 http://ukci2013.cs.surrey.ac.uk/ |
Workshop
Workshop | 13th UK Workshop on Computational Intelligence (UKCI) 2013 |
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Abbreviated title | UKCI 2013 |
Country/Territory | United Kingdom |
City | Guildford |
Period | 9/09/13 → 11/09/13 |
Internet address |
Keywords
- Vectors
- Semantics
- Measurment
- Natural language processing
- Educational institutions
- Media
- Electronic mail