Articles

We show that phrase structures in Penn Tree- bank style parses are not optimal for syntax- based machine translation. We exploit a se- ries of binarization methods to restructure the Penn Treebank style trees such that syn- tactified phrases smaller than Penn Treebank constituents can be acquired and exploited in …...

Syntactic reordering approaches are an ef- fective method for handling word-order dif- ferences between source and target lan- guages in statistical machine translation (SMT) systems. This paper introduces a re- ordering approach for translation from Chi- nese to English. We describe a set of syntac- tic reordering rules that exploit …...

This paper proposes a tree kernel with context- sensitive structured parse tree information for re- lation extraction. It resolves two critical problems in previous tree kernels for relation extraction in two ways. First, it automatically determines a dy- namic context-sensitive tree span for relation ex- traction by extending the widely-used …...

We present an information extraction system that decouples the tasks of finding relevant regions of text and applying extraction pat- terns. We create a self-trained relevant sen- tence classifier to identify relevant regions, and use a semantic affinity measure to au- tomatically learn domain-relevant extraction patterns. We then distinguish primary …...

In this paper, we analyze the effect of resampling techniques, including under- sampling and over-sampling used in active learning for word sense disambiguation (WSD). Experimental results show that un- der-sampling causes negative effects on ac- tive learning, but over-sampling is a rela- tively good choice. To alleviate the within- class …...

This paper presents a large-scale system for the recognition and semantic disambiguation of named entities based on information extracted from a large encyclopedic collection and Web search results. It describes in detail the disam- biguation paradigm employed and the information extraction process from Wikipedia. Through a process of maximizing the …...

We explore the use of Wikipedia as external knowledge to improve named entity recog- nition (NER). Our method retrieves the cor- responding Wikipedia entry for each can- didate word sequence and extracts a cate- gory label from the first sentence of the en- try, which can be thought of as …...

We present a nonparametric Bayesian model of tree structures based on the hierarchical Dirichlet process (HDP). Our HDP-PCFG model allows the complexity of the grammar to grow as more training data is available. In addition to presenting a fully Bayesian model for the PCFG, we also develop an ef- ficient …...

We consider the problem of learning to parse sentences to lambda-calculus repre- sentations of their underlying semantics and present an algorithm that learns a weighted combinatory categorial grammar (CCG). A key idea is to introduce non-standard CCG combinators that relax certain parts of the grammar—for example allowing flexible word order, …...

We introduce a technique for identifying the most salient participants in a discussion. Our method, MavenRank is based on lexical cen- trality: a random walk is performed on a graph in which each node is a participant in the discussion and an edge links two partici- pants who use similar …...

In this paper, we describe a new algorithm for recovering WH-trace empty nodes. Our approach combines a set of hand-written patterns together with a probabilistic model. Because the patterns heavily utilize regu- lar expressions, the pertinent tree structures are covered using a limited number of pat- terns. The probabilistic model …...

We propose a sequence-alignment based method for detecting and disambiguating co- ordinate conjunctions. In this method, av- eraged perceptron learning is used to adapt the substitution matrix to the training data drawn from the target language and domain. To reduce the cost of training data con- struction, our method accepts …...

We propose a novel method for Japanese de- pendency analysis, which is usually reduced to the construction of a dependency tree. In deterministic approaches to this task, depen- dency trees are constructed by series of ac- tions of attaching a bunsetsu chunk to one of the nodes in the tree …...

This paper proposes the use of Lexical- ized Tree-Adjoining Grammar (LTAG) for- malism as an important additional source of features for the Semantic Role Labeling (SRL) task. Using a set of one-vs-all Sup- port Vector Machines (SVMs), we evalu- ate these LTAG-based features. Our exper- iments show that LTAG-based features …...

Many systems for tasks such as question answer- ing, multi-document summarization, and infor- mation retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are based on only a single path between those words in the thesaurus graph. By contrast, we propose a new model …...

We present the idea of estimating seman- tic distance in one, possibly resource-poor, language using a knowledge source in an- other, possibly resource-rich, language. We do so by creating cross-lingual distributional profiles of concepts, using a bilingual lexi- con and a bootstrapping algorithm, but with- out the use of any …...

In this paper, we study the problem of auto- matically segmenting written text into para- graphs. This is inherently a sequence label- ing problem, however, previous approaches ignore this dependency. We propose a novel approach for automatic paragraph segmen- tation, namely training Semi-Markov mod- els discriminatively using a Max-Margin method. …...

This paper presents a method for catego- rizing named entities in Wikipedia. In Wikipedia, an anchor text is glossed in a linked HTML text. We formalize named en- tity categorization as a task of categorizing anchor texts with linked HTML texts which glosses a named entity. Using this repre- sentation, …...

A lexical analogy is a pair of word-pairs that share a similar semantic relation. Lex- ical analogies occur frequently in text and are useful in various natural language pro- cessing tasks. In this study, we present a system that generates lexical analogies au- tomatically from text data. Our system dis- …...

In this paper, we present a machine learn- ing approach to the identification and reso- lution of Chinese anaphoric zero pronouns. We perform both identification and resolu- tion automatically, with two sets of easily computable features. Experimental results show that our proposed learning approach achieves anaphoric zero pronoun resolution accuracy …...