SemAligner: A method and tool for aligning chunks with semantic relation types and semantic similarity scores
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.
Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
Maharjan, N., Banjade, R., Niraula, N., & Rus, V. (2016). SemAligner: A method and tool for aligning chunks with semantic relation types and semantic similarity scores. Proceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016, 1207-1211. Retrieved from https://digitalcommons.memphis.edu/facpubs/3194