You can generate the list of all papers by including:

{% include bibere/byyear.html %}

You can use mainAuthor, filterAuthor, and minYear to highlight an author, filter by an author, and only show latest papers, respectively.

For more details, see this page.

Publications (grouped by year): 2013, 2011, 2004, 2002.
2013
  • Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, Christopher Potts.Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. Empirical Methods in Natural Language Processing (EMNLP). 2013 Conference
    [ Website, Demo, Abstract, BibTex ]
    Semantic word spaces have been very useful but cannot express the meaning of longer phrases in a principled way. Further progress towards understanding compositionality in tasks such as sentiment detection requires richer supervised training and evaluation resources and more powerful models of composition. To remedy this, we introduce a Sentiment Treebank. It includes fine grained sentiment labels for 215,154 phrases in the parse trees of 11,855 sentences and presents new challenges for sentiment compositionality. To address them, we introduce the Recursive Neural Tensor Network. When trained on the new treebank, this model outperforms all previous methods on several metrics. It pushes the state of the art in single sentence positive/negative classification from 80% up to 85.4%. The accuracy of predicting fine-grained sentiment labels for all phrases reaches 80.7%, an improvement of 9.7% over bag of features baselines. Lastly, it is the only model that can accurately capture the effects of negation and its scope at various tree levels for both positive and negative phrases. 
    @inproceedings{recursive:emnlp13,
  author = {Richard Socher and Alex Perelygin and Jean Wu and Jason Chuang and Christopher D. Manning and Andrew Y. Ng and Christopher Potts},
  title = { {Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank} },
  booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2013}
}
2011
  • Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, Christopher Potts.Learning Word Vectors for Sentiment Analysis. Annual Meeting of the Association of Computational Linguistics (ACL). 2011 Conference
    [ BibTex ]
    @inproceedings{learning:acl11,
  author = {Andrew L. Maas and Raymond E. Daly and Peter T. Pham and Dan Huang and Andrew Y. Ng and Christopher Potts},
  title = { {Learning Word Vectors for Sentiment Analysis} },
  booktitle = {Annual Meeting of the Association of Computational Linguistics (ACL)},
  year = {2011}
}
2004
  • Bo Pang, Lillian Lee.A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Annual Meeting of the Association of Computational Linguistics (ACL). 2004 Conference
    [ PDF, Abstract, BibTex ]
    Sentiment analysis seeks to identify the viewpoint(s) underlying a text span; an example application is classifying a movie review as "thumbs up" or "thumbs down". To determine this sentiment polarity, we propose a novel machine-learning method that applies text-categorization techniques to just the subjective portions of the document. Extracting these portions can be implemented using efficient techniques for finding minimum cuts in graphs; this greatly facilitates incorporation of cross-sentence contextual constraints. 
    @inproceedings{education:acl04,
  author = {Bo Pang and Lillian Lee},
  title = { {A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts} },
  booktitle = {Annual Meeting of the Association of Computational Linguistics (ACL)},
  year = {2004}
}
2002
  • Bo Pang, Lillian Lee, Shivakumar Vaithyanathan.Thumbs up? Sentiment Classification using Machine Learning Techniques. Empirical Methods in Natural Language Processing (EMNLP). 2002 Conference
    [ BibTex ]
    @inproceedings{thumbs:emnlp02,
  author = {Bo Pang and Lillian Lee and Shivakumar Vaithyanathan},
  title = { {Thumbs up? Sentiment Classification using Machine Learning Techniques} },
  booktitle = {Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2002}
}