Monolingual (corpus-based) representations:

• M. Baroni and A. Lenci (2010): Distributional memory: A general framework for corpus-based semantics, Computational Linguistics, 36(4), pp.673-721.
• P. Turney and P. Pantel (2010): From Frequency to Meaning: Vector Space Models of Semantics, 37(1), pp.141-188.
• T. Mikolov, K. Chen, G. Corrado, and J. Dean (2013): Efficient Estimation of Word Representations in Vector Space, Proc. of ICLR.
• T. Mikolov, and J. Dean (2013): Distributed representations of words and phrases and their compositionality, Proc. of NIPS, pp. 3111–3119.
• J. Pennington, R. Socher, and C. D. Manning (2014): GloVe: Global Vectors for Word Representation, Proc. of EMNLP,  pp. 1532-1543.
• O. Levy, Y. Goldberg, and I. Dagan (2015): Improving distributional similarity with lessons learned from word embeddings, Transactions of the Association for Computational Linguistics, 3, pp.211-225.

Unified (cross-lingual) representations:

• T. Mikolov, Q. V. Le, and I. Sutskever (2013): Exploiting Similarities among Languages for Machine Translation, , arXiv preprint arXiv:1309.4168.
• W. Zou, R. Socher, D. Cer and C. D. Manning (2013): Bilingual Word Embeddings for Phrase-Based Machine Translation, Proc. of EMNLP,  pp. 1393-1398.
• R. Al-Rfou, B. Perozzi, and S. Skiena (2013): Polyglot: Distributed Word Representations for Multilingual NLP, Proc. of CONLL, pp. 183-192.
• S. Chandar, S. Lauly, H. Larochelle, M. Khapra, B. Ravindran, V. Raykar, and A. Saha (2014): An Autoencoder Approach to Learning Bilingual Word Representations, Proc. of NIPS, pp. 1853-1861.
• Stephan Gouws, Yoshua Bengio, and Greg Corrado (2014): BilBOWA: Fast Bilingual Distributed Representations without Word Alignments, Proc. of ICML, pp. 748-756.
• J. Camacho-Collados, M.T. Pilehvar and R. Navigli (2016): Nasari: Integrating explicit knowledge and corpus statistics for a multilingual representation of concepts and entities, Artificial Intelligence Journal 240, pp. 36-64.
• I. Vulić and M. Moens (2016): Bilingual Distributed Word Representations from Document-Aligned Comparable Data, Journal of Artificial Intelligence Research, 55, pp. 953-994.
• S. Upadhyay, M. Faruqui, C. Dyer, and D. Roth (2016): Cross-lingual Models of Word Embeddings: An Empirical Comparison, Proc. of ACL.

Semantic similarity techniques exploiting lexical resources:

• A. Budanitsky and G. Hirst (2006): Evaluating WordNet-based Measures of Lexical Semantic Relatedness, Computational Linguistics, 32(1), pp.13-47.
• E. Gabrilovich and S. Markovitch. (2007): Computing semantic relatedness using Wikipedia-based explicit semantic analysis, Proc. of IJCAI, pp. 1606-1611.
• E. Agirre, E. Alfonseca, K. Hall, J. Kravalova, M. Paşca, and A. Soroa (2009): A study on similarity and relatedness using distributional and wordnet-based approaches, Proc. of NAACL, pp. 19-27.
• M. T. Pilehvar, D. Jurgens and R. Navigli (2013): Align, Disambiguate and Walk: A Unified Approach for Measuring Semantic Similarity, Proc. of ACL, pp. 1341-1351.