WordNet: a lexical database for English
Communications of the ACM
The Journal of Machine Learning Research
Extracting structural paraphrases from aligned monolingual corpora
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Deeper natural language processing for evaluating student answers in intelligent tutoring systems
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Assessing Student Paraphrases Using Lexical Semantics and Word Weighting
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Automatic evaluation of topic coherence
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
LDA based similarity modeling for question answering
SS '10 Proceedings of the NAACL HLT 2010 Workshop on Semantic Search
Paraphrase identification on the basis of supervised machine learning techniques
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
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
Experiments with semantic similarity measures based on LDA and LSA
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
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We present in this paper the results of our investigation on semantic similarity measures at word- and sentence-level based on two fully-automated approaches to deriving meaning from large corpora: Latent Dirichlet Allocation, a probabilistic approach, and Latent Semantic Analysis, an algebraic approach. The focus is on similarity measures based on Latent Dirichlet Allocation, due to its novelty aspects, while the Latent Semantic Analysis measures are used for comparison purposes. We explore two types of measures based on Latent Dirichlet Allocation: measures based on distances between probability distribution that can be applied directly to larger texts such as sentences and a word-to-word similarity measure that is then expanded to work at sentence-level. We present results using paraphrase identification data in the Microsoft Research Paraphrase corpus.