IBM_EG-CORE: Comparing multiple Lexical and NE matching features in measuring Semantic Textual similarity

We present in this paper the systems we participated with in the Semantic Textual Similarity task at SEM 2013. The Semantic Textual Similarity Core task (STS) computes the degree of semantic equivalence between two sentences where the participant systems will be compared to the manual scores, which range from 5 (semantic equivalence) to 0 (no relation). We combined multiple text similarity measures of varying complexity. The experiments illustrate the different effect of four feature types including direct lexical matching, idf-weighted lexical matching, modified BLEU N-gram matching and named entities matching. Our team submitted three runs during the task evaluation period and they ranked number 11, 15 and 19 among the 90 participating systems according to the official Mean Pearson correlation metric for the task. We also report an unofficial run with mean Pearson correlation of 0.59221 on STS2013 test dataset, ranking as the 3 best system among the 90 participating systems.