Tuesday, January 15, 2013

Notes on COLING 2012 - Part 2

Inducing Crosslingual Distributed Representations of Words. Alexandre Klementiev, Ivan Titov, Binod Bhattarai.

  • Problem: Learning a semantic space where points represent words, and similar words are nearby
  • Key ideas:
    •  Use deep learning (neural network-based) to learn low-dimensional (d) representation of words (d fixed arbitrarily). 
    • Do the above in a multi-talk learning setting to learn the low-d representation that holds across languages.
    • Use a parallel corpus to learn a similarity matrix between words---used for training the multi-task+neural-net model.
  • Found several interesting papers that might be worth reading (esp. starred ones)
    • * Täckström, O., McDonald, R., and Uszkoreit, J. Cross-lingual word clusters for direct transfer of linguistic structure. NAACL 2012
    • * Turian, J., Ratinov, L., and Bengio, Y. Word representations: a simple and general method for semi-supervised learning. ACL 2010
    • * Fouss, F., Pirotte, A., Renders, J., and Saerens, M. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE KDE 2007
    • Cavallanti, G., Cesa-bianchi, N., and Gentile, C. Linear algorithms for online multitask classification. JMLR 2010
    • Socher, R., Huang, E. H., Pennin, J., Ng, A. Y., and Manning, C. D. Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. NIPS 2011
    • Huang, E., Socher, R., Manning, C., and Ng, A. Improving word representations via global context and multiple word prototypes. ACL 2012
    • Callison-Burch, C., Koehn, P Monz, C., Post, M., Soricut, R., and Specia, L. Findings of the 2012 workshop on statistical machine translation. WMT ACL 2012. (see for preprocessing steps)
    • Shi, L., Mihalcea, R., and Tian, M. Cross language text classification by model translation and semi-supervised learning. EMNLP 2010
    • Titov, I. Domain adaptation by constraining inter-domain variability of latent feature representation. ACL 2011
    • Glorot, X., Bordes, A., and Bengio, Y. Domain adaptation for large-scale sentiment classification: A deep learning approach. ICML 2011
    • Zhang, D., Mei, Q., and Zhai, C. Cross-lingual latent topic extraction. ACL 2010
    • Fortuna, B. and Shawe-Taylor, J. The use of machine translation tools for cross-lingual text mining. Workshop on Learning with Multiple Views, ICML 2005

Long-tail Distributions and Unsupervised Learning of Morphology. Qiuye Zhao, Mitch Marcus

  • Problem: Learning unsupervised morphological analyzers. Previous work assumed power-law distributions for rank-frequency of morph units. They propose log-normal distribution instead.
  • Comments: Current approaches to morph analysis have moved beyond ILP and finite state machines. Need to do background reading to understand this work, e.g. Chan, E. Structures and distributions in morphology learning. PhD thesis 2008.

Graph-based Multi-tweet Summarization Using Social Signals. LIU XiaoHua LI Yi Tong WEI FuRu ZHOU Ming.

  • Problem: Given a set of tweets, find one that is representative of the lot.
  • Approach: Uses scoring functions and features tailored for the problem taking into account saliency, readibility, tweeter diversity, and uses existing work on multi-document summarization and on tweets.
  • Comments: Check out user study.

To Exhibit is not to Loiter: A Multilingual, Sense-Disambiguated Wiktionary for Measuring Verb Similarity. Christian M. Meyer, Iryna Gurevych

  • Problem:
    • Given links between words, identify which senses of the words are actually (supposed to have been) linked.
    • Given links between senses of words, infer new links, e.g. between words in different languages.
    • Given links between senses of words, compute verb similarity.
  • Key Ideas: Start with a dictionary with partial sense information. Disambiguate (remove incorrect) and infer (add new) links.
  • Comments: Check out resources created.
  • Interesting papers mentioned
    • computing semantic relatedness by measuring path lengths (Budanitsky and Hirst, 2006)

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