Add to ORKGWe discuss a two-stage disambiguation technique for linguistically precise broad-coverage grammars: the pre-filter of the first stage is triggered by linguistic configurations (“optimality marks”) specified by the grammar writer; the second stage is a log-linear probability model trained on corpus data. This set-up is used in the Parallel Grammar (ParGram) project, developing Lexical Functional Grammars for various languages. The present paper is the first study exploring how the pre-filter can be empirically tuned by learning a relative ranking of the optimality marks from corpus data, identifying problematic marks and relaxing the filter in various ways.
We present a method of applying a broad-coverage LFG grammar of German in the process of semi-automa...
The challenge of simultaneously learning a lexicon of underlying forms and a constraint ranking has ...
This thesis focuses on the development of effective and efficient language models (LMs) for speech r...
Developing large-scale deep grammars in a constraint-based framework such as Lexical Functional Gram...
In this paper we present a new method for machine learning-based optimization of linguist-written Co...
Natural Language is highly ambiguous, on every level. This article describes a fast broad-coverage s...
In this work, we apply statistical learning algorithms to Lexicalized Tree Adjoining Grammar (LTAG) ...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the auto-...
This thesis develops a new approach to the problem of indeterminacy in grammar-based natural languag...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the autom...
This article uses semi-supervised Expectation Maximization (EM) to learn lexico-syntactic dependenci...
Theoretical thesis.Bibliography pages: 191-2041. Introduction -- 2. Literature review -- 3. Grammati...
Ambiguity resolution in the parsing of natural language requires a vast repository of knowledge to g...
Traditionally, deep, wide-coverage linguistic resources are hand-crafted and their creation is time-...
ii This dissertation undertakes the full formal problem of phonological learning- the learning of ph...
We present a method of applying a broad-coverage LFG grammar of German in the process of semi-automa...
The challenge of simultaneously learning a lexicon of underlying forms and a constraint ranking has ...
This thesis focuses on the development of effective and efficient language models (LMs) for speech r...
Developing large-scale deep grammars in a constraint-based framework such as Lexical Functional Gram...
In this paper we present a new method for machine learning-based optimization of linguist-written Co...
Natural Language is highly ambiguous, on every level. This article describes a fast broad-coverage s...
In this work, we apply statistical learning algorithms to Lexicalized Tree Adjoining Grammar (LTAG) ...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the auto-...
This thesis develops a new approach to the problem of indeterminacy in grammar-based natural languag...
This paper examines the usefulness of corpus-derived probabilistic grammars as a basis for the autom...
This article uses semi-supervised Expectation Maximization (EM) to learn lexico-syntactic dependenci...
Theoretical thesis.Bibliography pages: 191-2041. Introduction -- 2. Literature review -- 3. Grammati...
Ambiguity resolution in the parsing of natural language requires a vast repository of knowledge to g...
Traditionally, deep, wide-coverage linguistic resources are hand-crafted and their creation is time-...
ii This dissertation undertakes the full formal problem of phonological learning- the learning of ph...
We present a method of applying a broad-coverage LFG grammar of German in the process of semi-automa...
The challenge of simultaneously learning a lexicon of underlying forms and a constraint ranking has ...
This thesis focuses on the development of effective and efficient language models (LMs) for speech r...