July 15, 2012

ACL-EMNLP 2012 Highlights

We demonstrated 80% unsupervised part-of-speech induction accuracy in our EMNLP-2012 paper using paradigmatic representations of word context and co-occurrence modeling. Here are some interesting talks I attended at ACL-EMNLP this year.

Check out this tutorial:

Inderjeet Mani; James Pustejovsky
Qualitative Modeling of Spatial Prepositions and Motion Expressions

And this paper from today on learning language from navigation instructions and user behavior was interesting:

David Chen
Fast Online Lexicon Learning for Grounded Language Acquisition

All papers can be found on the ACL Anthology page:

Here is a work that builds on CCM for unsupervised parsing that works better on longer sentences:

P12-2004 [bib]: Dave Golland; John DeNero; Jakob Uszkoreit
A Feature-Rich Constituent Context Model for Grammar Induction

Here is another interesting talk about grounded language acquisition, computer learning to follow instructions in a virtual world. They have collected nice corpora of instructions and behaviors of people following those instructions.

P12-2036 [bib]: Luciana Benotti; Martin Villalba; Tessa Lau; Julian Cerruti
Corpus-based Interpretation of Instructions in Virtual Environments

And here is another similar work on grounded language acquisition:

P12-1045 [bib]: David Chen
Fast Online Lexicon Learning for Grounded Language Acquisition

I nominate this as the best paper on computer humor generation of ACL 2012. Note the resources ConceptNet and SentiNet mentioned in this work which may be independently useful.

P12-2030 [bib]: Igor Labutov; Hod Lipson
Humor as Circuits in Semantic Networks

We have been working on reordering for SMT. Here is a paper that modifies distortion matrices instead to allow more flexible reorderings.

P12-1050 [bib]: Arianna Bisazza; Marcello Federico
Modified Distortion Matrices for Phrase-Based Statistical Machine Translation

Interesting tree transformation operations.

Transforming Trees to Improve Syntactic Convergence
D. Burkett and D. Klein .

The following paper utilizes n-gram language models in unsupervised dependency parsing:

D12-1028 [bib]: David Mareček; Zdeněk Žabokrtský
Exploiting Reducibility in Unsupervised Dependency Parsing

Another reordering paper from EMNLP. Mentions a string to tree version of Moses that is publicly available.

D12-1077 [bib]: Graham Neubig; Taro Watanabe; Shinsuke Mori
Inducing a Discriminative Parser to Optimize Machine Translation Reordering

Must read paper from emnlp. Very likely our paradigmatic representation would do better here.

D12-1130 [bib]: Carina Silberer; Mirella Lapata
Grounded Models of Semantic Representation

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July 12, 2012

Learning Syntactic Categories Using Paradigmatic Representations of Word Context

Mehmet Ali Yatbaz, Enis Sert, Deniz Yuret. EMNLP 2012. (Download the paper, presentation, code, fastsubs paper, lm training data (250MB), wsj substitute data (1GB), scode output word vectors (5MB), scode visualization demo (may take a few minutes to load). More up to date versions of the code can be found at github.)

Abstract: We investigate paradigmatic representations of word context in the domain of unsupervised syntactic category acquisition. Paradigmatic representations of word context are based on potential substitutes of a word in contrast to syntagmatic representations based on properties of neighboring words. We compare a bigram based baseline model with several paradigmatic models and demonstrate significant gains in accuracy. Our best model based on Euclidean co-occurrence embedding combines the paradigmatic context representation with morphological and orthographic features and achieves 80% many-to-one accuracy on a 45-tag 1M word corpus.

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