Articles

This paper presents a set of algorithms for distinguishing personal names with mul- tiple real referents in text, based on little or no supervision. The approach utilizes an unsupervised clustering technique over a rich feature space of biographic facts, which are automatically extracted via a language-independent bootstrapping pro- cess. The …...

We explore the idea of creating a subjectiv- ity classifier that uses lists of subjective nouns learned by bootstrapping algorithms. The goal of our research is to develop a system that can distinguish subjective sentences from ob- jective sentences. First, we use two bootstrap- ping algorithms that exploit extraction patterns …...

We describe new features and algorithms for HPSG parse selection models and address the task of creating annotated material to train them. We evaluate the ability of several sam- ple selection methods to reduce the number of annotated sentences necessary to achieve a given level of performance. Our best method …...

This paper presents a new approach to syntac- tic disambiguation based on lexicalized gram- mars. While existing disambiguation mod- els decompose the probability of parsing re- sults into that of primitive dependencies of two words, our model selects the most probable parsing result from a set of candidates allowed by …...

This paper introduces a novel Support Vec- tor Machines (SVMs) based voting algorithm for reranking, which provides a way to solve the sequential models indirectly. We have presented a risk formulation under the PAC framework for this voting algorithm. We have applied this algorithm to the parse reranking problem, and …...

This paper examines feature selection for log linear models over rich constraint-based grammar (HPSG) representations by building decision trees over fea- tures in corresponding probabilistic context free grammars (PCFGs). We show that single decision trees do not make optimal use of the available in- formation; constructed ensembles of decision trees …...

Words differ in the subcategorisation frames in which they occur, and there is a strong cor- relation between the semantic arguments of a given word and its subcategorisation frame, so that all its arguments should be included in its subcategorisation frame. One problem is posed by the ambiguity between locative …...

The regularity of named entities is used to learn names and to extract named entities. Having only a few name elements and a set of patterns the al- gorithm learns new names and its elements. A verification step assures quality using a large background corpus. Further improvement is reached through …...

In this paper, we propose a new statistical Japanese dependency parser using a cascaded chunking model. Conventional Japanese statistical depen- dency parsers are mainly based on a probabilistic model, which is not always efficient or scalable. We propose a new method that is simple and efficient, since it parses a …...

Conditional maximum entropy (ME) models pro- vide a general purpose machine learning technique which has been successfully applied to fields as diverse as computer vision and econometrics, and which is used for a wide variety of classification problems in natural language processing. However, the flexibility of ME models is not …...

We present a method for identifying corresponding themes across several corpora that are focused on related, but distinct, domains. This task is approached through simultaneous clustering of keyword sets extracted from the analyzed corpora. Our algorithm extends the information- bottleneck soft clustering method for a suitable setting consisting of several …...

Banko and Brill (2001) suggested that the develop- ment of very large training corpora may be more ef- fective for progress in empirical Natural Language Processing than improving methods that use exist- ing smaller training corpora. This work tests their claim by exploring whether a very large corpus can eliminate …...

This paper investigates the use of a language inde- pendent model for named entity recognition based on iterative learning in a co-training fashion, using word-internal and contextual information as inde- pendent evidence sources. Its bootstrapping pro- cess begins with only seed entities and seed con- texts extracted from the provided …...

This paper presents a method for bootstrapping a fine-grained, broad-coverage part-of-speech (POS) tagger in a new language using only one person- day of data acquisition effort. It requires only three resources, which are currently readily available in 60-100 world languages: (1) an online or hard-copy pocket-sized bilingual dictionary, (2) a …...

This paper proposes a series of techniques for ex- tracting English verb–particle constructions from raw text corpora. We initially propose three basic methods, based on tagger output, chunker output and a chunk grammar, respectively, with the chunk grammar method optionally combining with an at- tachment resolution module to determine the …...

This paper presents the ongoing project Computational Models of First Language Acquisition, together with its current product, the learning algorithm GraSp. GraSp is designed specifically for inducing grammars from large, unlabelled corpora of spontaneous (i.e. unscripted) speech. The learning algorithm does not assume a predefined grammatical taxonomy; rather the determination …...

Voicemail is not like email. Even such ba- sic information as the name of the caller/ sender or a phone number for returning calls is not represented explicitly and must be obtained from message transcripts or other sources. We discuss techniques for doing this and the challenges these tasks present. …...

We report on experiments in reference res- olution using a decision tree approach. We started with a standard feature set used in previous work, which led to moderate re- sults. A closer examination of the perfor- mance of the features for different forms of anaphoric expressions showed good re- sults …...

There has been much interest in us- ing phrasal movement to improve statis- tical machine translation. We explore how well phrases cohere across two lan- guages, specifically English and French, and examine the particular conditions un- der which they do not. We demonstrate that while there are cases where coherence …...

In this paper, we propose a method for learning a classifier which combines out- puts of more than one Japanese named entity extractors. The proposed combi- nation method belongs to the family of stacked generalizers, which is in principle a technique of combining outputs of sev- eral classifiers at the …...