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

SemAligner: A Method and Tool for Aligning Chunks with Semantic Relation Types and Semantic Similarity Scores Nabin Maharjan, Rajendra Banjade, Nobal B. Niraula, Vasile Rus Department of Computer Science, Institute for Intelligent Systems The University of Memphis, USA E-mail: {nmharjan,rbanjade,nbnraula,vrus}@memphis.edu 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. Keywords: Chunk alignment, Chunk semantic relations, Interpretable semantic similarity 1. Introduction This paper presents a textual chunk alignment method and software tool called SemAligner which can align textual chunks in a given pair of texts (chunked or plain texts). A chunk is a syntactically meaningful unit which typically consists of a single content word surrounded by a group of function words (Abney, 1991). The SemAligner also assigns semantic relation types and semantic similarity scores to the aligned chunks; thus, the proposed SemAligner tool creates a new category of natural language processing tools called semantic aligners. It should be noted that there exist word alignment tools but they do not assign relation types between aligned tokens, a limitation that hampers their usefulness as explained later. There is an acute need for accurate semantic aligners. For instance, labeling aligned chunks with the underlying semantic relation type and computing semantic similarity scores for them would be extremely useful for explaining or interpreting why two texts are similar or dissimilar. Indeed, existing semantic textual similarity (STS; Agirre et. al. 2015) systems can quantify the similarity between given text-pairs but do not explain in what ways they are similar, related or unrelated. An explanatory layer would make a big difference in many Natural Language Processing (NLP) applications such as intelligent tutoring system (Graesser et al. 2012; Rus et al. 2013) and student answer evaluation (Rus et. al. 2006; Nielsen 2009). An explanatory layer would transform NLP applications from black boxes into systems that would act intelligently as they would be able to explain their decisions. The organization of Interpretable Short Text Similarity (iSTS) task at SemEval 2015 (Agirre et. al. 2015) highlights the need for such an explanatory layer in STS systems. We originally developed SemAligner as an entry system in the pilot iSTS shared task. Our tool emerged as the top performing system among the …

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