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Our goal is to extract answers from pre-retrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer types. This casts answer extraction as an answer sequence tagging problem for the first time,(More)
Answering natural language questions using the Freebase knowledge base has recently been explored as a platform for advancing the state of the art in open domain semantic parsing. Those efforts map questions to sophisticated meaning representations that are then attempted to be matched against viable answer candidates in the knowledge base. Here we show(More)
We propose the use of a nonparametric Bayesian model, the Hierarchical Dirichlet Process (HDP), for the task of Word Sense Induction. Results are shown through comparison against Latent Dirich-let Allocation (LDA), a parametric Bayesian model employed by Brody and Lapata (2009) for this task. We find that the two models achieve similar levels of induction(More)
We introduce a novel discriminative model for phrase-based monolingual alignment using a semi-Markov CRF. Our model achieves state-of-the-art alignment accuracy on two phrase-based alignment datasets (RTE and paraphrase), while doing significantly better than other strong baselines in both non-identical alignment and phrase-only alignment. Additional(More)
Fast alignment is essential for many natural language tasks. But in the setting of monolingual alignment, previous work has not been able to align more than one sentence pair per second. We describe a dis-criminatively trained monolingual word aligner that uses a Conditional Random Field to globally decode the best alignment with features drawn from source(More)
  • Xuchen Yao, Jianqiang Ma, Sergio Duarte, Ça Grı Çöltekin
  • 2009
We propose a learning method with categorial grammars using inference rules. The proposed learning method has been tested on an artificial language fragment that contains both ambiguity and recursion. We demonstrate that our learner has successfully converged to the target grammar using a relatively small set of initial assumptions. We also show that our(More)
  • Xuchen Yao, Jianqiang Ma, Sergio Duarte, Ça Grı Çöltekin
  • 2009
We propose an unsupervised inference rules-based categorial grammar learning method, which aims to simulate language acquisition. The learner has been trained and tested on an artificial language fragment that contains both ambiguity and re-cursion. We demonstrate that the learner has 100% coverage with respect to the target grammar using a relatively small(More)
We contrast two seemingly distinct approaches to the task of question answering (QA) using Freebase: one based on information extraction techniques, the other on semantic parsing. Results over the same test-set were collected from two state-of-the-art, open-source systems, then analyzed in consultation with those systems' creators. We conclude that the(More)
Information Retrieval (IR) and Answer Extraction are often designed as isolated or loosely connected components in Question Answering (QA), with repeated over-engineering on IR, and not necessarily performance gain for QA. We propose to tightly integrate them by coupling automatically learned features for answer extraction to a shallow-structured IR model.(More)