Sentence alignment for monolingual comparable corpora
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Corpus-based and knowledge-based measures of text semantic similarity
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Text-to-text semantic similarity for automatic short answer grading
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Using measures of semantic relatedness for word sense disambiguation
CICLing'03 Proceedings of the 4th international conference on Computational linguistics and intelligent text processing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
The PASCAL recognising textual entailment challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
SemEval-2012 task 6: a pilot on semantic textual similarity
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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In this paper we present our systems for the STS task. Our systems are all based on a simple process of identifying the components that correspond between two sentences. Currently we use words (that is word forms), lemmas, distributional similar words and grammatical relations identified with a dependency parser. We submitted three systems. All systems only use open class words. Our first system (alignheuristic) tries to obtain a mapping between every open class token using all the above sources of information. Our second system (wordsim) uses a different algorithm and unlike alignheuristic, it does not use the dependency information. The third system (average) simply takes the average of the scores for each item from the other two systems to take advantage of the merits of both systems. For this reason we only provide a brief description of that. The results are promising, with Pearson's coefficients on each individual dataset ranging from .3765 to .7761 for our relatively simple heuristics based systems that do not require training on different datasets. We provide some analysis of the results and also provide results for our data using Spearman's, which as a nonparametric measure which we argue is better able to reflect the merits of the different systems (average is ranked between the others).