Multiple Alignment of Citation Sentences with Conditional Random Fields and Posterior Decoding

In scientific literature, sentences that cite related work can be a valuable resource for applications such as summarization, synonym identification, and entity extraction. In order to determine which equivalent entities are discussed in the various citation sentences, we propose aligning the words within these sentences according to semantic similarity. This problem is partly analogous to the problem of multiple sequence alignment in the biosciences, and is also closely related to the word alignment problem in statistical machine translation. In this paper we address the problem of multiple citation concept alignment by combining and modifying the CRF based pairwise word alignment system of Blunsom & Cohn (2006) and a posterior decoding based multiple sequence alignment algorithm of Schwartz & Pachter (2007). We evaluate the algorithm on hand-labeled data, achieving results that improve on a baseline.