SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet and Word Embedding for Semantic Textual Similarity

摘要

In this paper, a system for semantic textual similarity, which participated in Task-1 in SemEval 2016 (monolingual and cross-lingual sub-tasks) is described. The system contains a preprocessing step that simplifies text using PPDB 2.0 and detects negations. Also, six lexical similarity functions were constructed using string matching, word embedding and synonyms-antonyms relations in WordNet. These lexical similarity functions are projected to sentence level using a new method called Polarized Soft Cardinality that supports negative similarities between words to model opposites. We also introduce a novel L~2-norm "cardinality" for vector space representations. The system extracts a set of 660 features from each pair of text snippets using the proposed cardinality measures. From this set, a subset of 12 features was selected in a supervised manner. These features are combined by SVR and, alternatively, by using the arithmetic mean to produce similarity predictions. Our team ranked second in the cross-lingual sub-task and got close to the best official results in the monolingual sub-task.