SemAligner: A Method and Tool for Aligning Chunks with Semantic Relation Types and Semantic Similarity Scores

Nabin Maharjan, Rajendra Banjade, Nobal Bikram Niraula, Vasile Rus


Abstract
This paper introduces a ruled-based method and software tool, called SemAligner, for aligning chunks across texts in a given pair of short English texts. The tool, based on the top performing method at the Interpretable Short Text Similarity shared task at SemEval 2015, where it was used with human annotated (gold) chunks, can now additionally process plain text-pairs using two powerful chunkers we developed, e.g. using Conditional Random Fields. Besides aligning chunks, the tool automatically assigns semantic relations to the aligned chunks (such as EQUI for equivalent and OPPO for opposite) and semantic similarity scores that measure the strength of the semantic relation between the aligned chunks. Experiments show that SemAligner performs competitively for system generated chunks and that these results are also comparable to results obtained on gold chunks. SemAligner has other capabilities such as handling various input formats and chunkers as well as extending lookup resources.
Anthology ID:
L16-1192
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
1207–1211
Language:
URL:
https://www.aclweb.org/anthology/L16-1192
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/L16-1192.pdf