Literature and References

We have compiles a list of publications regarding JoBimText and its use cases, as well as theoretical literature on Distributional Semantics, Contextualization and Sense Induction.


  • Riedl M., Steuer R., Biemann C. (2014): Distributed Distributional Similarities of Google Books over Centuries. Proceedings Fourth International Conference on Language Resources and Evaluation (LREC 2014), Reykjavik, Iceland. ( pdf resources)
  • Gliozzo A., Biemann C, Riedl M., Coppola B., Glass M. R., Hatem M. (2013): JoBimText Visualizer: A Graph-based Approach to Contextualizing Distributional Similarity. Proceedings of the 8th Workshop on TextGraphs in conjunction with EMNLP 2013 (pdf)
  • Biemann C., Riedl M (2013): From Global to Local Similarities: A Graph-Based Contextualization Method using Distributional Thesauri. Proceedings of the 8th Workshop on TextGraphs in conjunction with EMNLP 2013 (pdf)
  • Riedl M., Biemann C. (2013): Scaling to Large^3 Data: An efficient and effective method to compute Distributional Thesauri. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, EMNLP 2013 (pdf)
  • Journal Article: Biemann, C., Riedl, M. (2013): Text: Now in 2D! A Framework for  Lexical Expansion with Contextual Similarity. Journal of Language Modelling 1(1):55–95 (pdf)
  • Slides from the Two-Days Tutorial on Watson and the DeepQA Architecture, March 18/19, 2013, TU Darmstadt
  • “Beyond Jeopardy! – Adapting Watson to new domains using Distributional Semantics” – slides of Alfio Gliozzo’s talk at ICSI Berkeley, November 2012
  • “Text: Now in 2D — Lexical Expansion using Contextual Similarity” – slides of Chris Biemann’s talk at ETS Princeton, September 2012

Linguistics, Distributional Semantics, Structuralism

  • de Saussure, F. (1916). Cours de linguistique générale. Librairie Payot & Cie, Paris.
  • Z. Harris. (1954). Distributional Structure. Word 10 (2/3)
  • G. A. Miller, W. G. Charles (1991): Contextual Correlates of Semantic Similarity. Language and Cognitive Processes 1991, 6 (1) 1-28
  • Biemann, C. (2011): Structure Discovery in Natural Language. In G. Hirst, E. Hovy and M. Johnson (Series Eds.): Theory and Applications of Natural Language Processing, Springer Heidelberg Dordrecht London New York
  • Gliozzo, A., Strapparava, C. (2009): Semantic Domains in Computational Linguistics. Springer. ISBN: 978-3-540-68156-4

Distributional Similarity

  • Lin, D. (1998). Automatic retrieval and clustering of similar words. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics, volume 2 of ACL ’98, pages 768–774, Stroudsburg, PA, USA. Association for Computational Linguistics.
  • Bär, D., Biemann, C., Gurevych, I., and Zesch, T. (2012). UKP: Computing semantic textual similarity by combining multiple content similarity measures. In Proceedings of the 6th International Workshop on Semantic Evaluation, pages 435–440.

Sense Induction

  • Biemann, C. (2010): Co-occurrence Cluster Features for Lexical Substitutions in Context. Proceedings of the 5th Workshop on TextGraphs in conjunction with ACL 2010, Uppsala, Sweden
  • Biemann, C. (2006): Chinese Whispers – an Efficient Graph Clustering Algorithm and its Application to Natural Language Processing Problems. Proceedings of the HLT-NAACL-06 Workshop on Textgraphs-06, New York, USA
  • Widdows, D. and Dorow, B. (2002): A graph model for unsupervised lexical acquisition. In Proceedings of the 19th international conference on Computational linguistics – Volume 1 (COLING ’02), Vol. 1.

Machine Learning for Contextualization

  • Viterbi A.J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory 13 (2): 260–269. doi:10.1109/TIT.1967.1054010
  • Hastings, W.K. (1970). “Monte Carlo Sampling Methods Using Markov Chains and Their Applications”. Biometrika 57 (1): 97–109. doi:10.1093/biomet/57.1.97
  • Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022.

Additional Literature

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