We describe a parser that draws from both extant corpora and linguistic knowledge sources, and thus is suitable as a front end for applications requiring both broad coverage and rich syntactic analysis. We detail many of the difficulties and assumptions involved in combining these data and knowledge sources. We also describe the novel language model that we use for disambiguation and show that it outperforms a comparable model without the same knowledge sources
Statistical parsers are e ective but are typically limited to producing projective dependencies or c...
This study shows that using computational linguistic models is beneficial for descriptive linguistic...
In this paper we will present an approach to natural language processing which we define as "hybrid"...
Statistical techniques have revolutionized all areas of natural language processing, and syntactic p...
Natural Language is highly ambiguous, on every level. This article describes a fast broad-coverage s...
For years, researchers have used knowledge-intensive techniques for disambiguating during parsing. T...
Statistical models for parsing natural language have recently shown considerable success in broad-co...
Applying statistical parsers developed for English to languages with freer word-order has turned out...
We present the first application of the head-driven statistical parsing model of Collins (1999) as a...
Language models are an important component of speech recognition. They aim to predict the probabilit...
Data-oriented models of language processing embody the assumption that human language perception and...
This paper describes a rst attempt at a sta-tistical model for simultaneous syntactic pars-ing and g...
We present the first application of the head-driven statistical parsing model of Collins (1999) as a...
Developing tools for doing computational linguistics work in low-resource scenarios often requires c...
Statistical parsers are effective but are typically limited to producing projective dependencies or ...
Statistical parsers are e ective but are typically limited to producing projective dependencies or c...
This study shows that using computational linguistic models is beneficial for descriptive linguistic...
In this paper we will present an approach to natural language processing which we define as "hybrid"...
Statistical techniques have revolutionized all areas of natural language processing, and syntactic p...
Natural Language is highly ambiguous, on every level. This article describes a fast broad-coverage s...
For years, researchers have used knowledge-intensive techniques for disambiguating during parsing. T...
Statistical models for parsing natural language have recently shown considerable success in broad-co...
Applying statistical parsers developed for English to languages with freer word-order has turned out...
We present the first application of the head-driven statistical parsing model of Collins (1999) as a...
Language models are an important component of speech recognition. They aim to predict the probabilit...
Data-oriented models of language processing embody the assumption that human language perception and...
This paper describes a rst attempt at a sta-tistical model for simultaneous syntactic pars-ing and g...
We present the first application of the head-driven statistical parsing model of Collins (1999) as a...
Developing tools for doing computational linguistics work in low-resource scenarios often requires c...
Statistical parsers are effective but are typically limited to producing projective dependencies or ...
Statistical parsers are e ective but are typically limited to producing projective dependencies or c...
This study shows that using computational linguistic models is beneficial for descriptive linguistic...
In this paper we will present an approach to natural language processing which we define as "hybrid"...