Articles

Previous research applying kernel meth- ods to natural language parsing have fo- cussed on proposing kernels over parse trees, which are hand-crafted based on do- main knowledge and computational con- siderations. In this paper we propose a method for defining kernels in terms of a probabilistic model of parsing. This …...

Discriminative reranking is one method for constructing high-performance statis- tical parsers (Collins, 2000). A discrim- inative reranker requires a source of can- didate parses for each sentence. This pa- per describes a simple yet novel method for constructing sets of 50-best parses based on a coarse-to-fine generative parser (Charniak, 2000). …...

In this paper we focus on how to improve pronoun resolution using the statistics- based semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such in- formation: statistics source and learning framework. Specifically, we for the first time propose to utilize the web and the twin-candidate …...

In this paper, we view coreference reso- lution as a problem of ranking candidate partitions generated by different coref- erence systems. We propose a set of partition-based features to learn a rank- ing model for distinguishing good and bad partitions. Our approach compares fa- vorably to two state-of-the-art coreference systems …...

This paper considers the problem of auto- matic assessment of local coherence. We present a novel entity-based representa- tion of discourse which is inspired by Cen- tering Theory and can be computed au- tomatically from raw text. We view co- herence assessment as a ranking learning problem and show that …...

Processing discourse connectives is im- portant for tasks such as discourse parsing and generation. For these tasks, it is use- ful to know which connectives can signal the same coherence relations. This paper presents experiments into modelling the substitutability of discourse connectives. It shows that substitutability effects dis- tributional similarity. …...

To facilitate the use of syntactic infor- mation in the study of child language acquisition, a coding scheme for Gram- matical Relations (GRs) in transcripts of parent-child dialogs has been proposed by Sagae, MacWhinney and Lavie (2004). We discuss the use of current NLP tech- niques to produce the GRs …...

This paper introduces a new application of boost- ing for parse reranking. Several parsers have been proposed that utilize the all-subtrees representa- tion (e.g., tree kernel and data oriented parsing). This paper argues that such an all-subtrees repre- sentation is extremely redundant and a compara- ble accuracy can be achieved …...

We propose a method for extracting se- mantic orientations of words: desirable or undesirable. Regarding semantic ori- entations as spins of electrons, we use the mean field approximation to compute the approximate probability function of the system instead of the intractable ac- tual probability function. We also pro- pose a …...

In this paper, we present an unsupervised methodology for propagating lexical co- occurrence vectors into an ontology such as WordNet. We evaluate the framework on the task of automatically attaching new concepts into the ontology. Experimental results show 73.9% attachment accuracy in the first position and 81.3% accuracy in the …...

This paper suggests refinements for the Distributional Similarity Hypothesis. Our proposed hypotheses relate the distribu- tional behavior of pairs of words to lexical entailment – a tighter notion of semantic similarity that is required by many NLP applications. To automatically explore the validity of the defined hypotheses we de- veloped …...

We address the rating-inference problem, wherein rather than simply decide whether a review is “thumbs up” or “thumbs down”, as in previous sentiment analy- sis work, one must determine an author’s evaluation with respect to a multi-point scale (e.g., one to five “stars”). This task represents an interesting twist on …...

In order to realize the full potential of dependency-based syntactic parsing, it is desirable to allow non-projective depen- dency structures. We show how a data- driven deterministic dependency parser, in itself restricted to projective structures, can be combined with graph transforma- tion techniques to produce non-projective structures. Experiments using data …...

We present an effective training al- gorithm for linearly-scored dependency parsers that implements online large- margin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for de- pendency trees (Eisner, 1996). The trained parsers achieve a competitive dependency accuracy for both English and …...

This paper reports the development of log- linear models for the disambiguation in wide-coverage HPSG parsing. The esti- mation of log-linear models requires high computational cost, especially with wide- coverage grammars. Using techniques to reduce the estimation cost, we trained the models using 20 sections of Penn Tree- bank. A …...

This paper defines a generative probabilis- tic model of parse trees, which we call PCFG-LA. This model is an extension of PCFG in which non-terminal symbols are augmented with latent variables. Fine- grained CFG rules are automatically in- duced from a parsed corpus by training a PCFG-LA model using an …...

To improve the interaction between students and an intelligent tutoring system, we devel- oped two Natural Language generators, that we systematically evaluated in a three way com- parison that included the original system as well. We found that the generator which intu- itively produces the best language does engen- der …...

We describe an automatic Word Sense Disambiguation (WSD) system that dis- ambiguates verb senses using syntactic and semantic features that encode infor- mation about predicate arguments and se- mantic classes. Our system performs at the best published accuracy on the English verbs of Senseval-2. We also experiment with using the …...

Word Sense Disambiguation suffers from a long-standing problem of knowledge ac- quisition bottleneck. Although state of the art supervised systems report good accu- racies for selected words, they have not been shown to be promising in terms of scalability. In this paper, we present an ap- proach for learning coarser …...

The limited coverage of lexical-semantic re- sources is a significant problem for NLP sys- tems which can be alleviated by automati- cally classifying the unknown words. Su- persense tagging assigns unknown nouns one of 26 broad semantic categories used by lex- icographers to organise their manual inser- tion into WORDNET. …...