Paper Info
Title
Augmenting a hidden Markov model for phrase-dependent word tagging
Abstract
The paper describes refinements that are currently being investigated in a model for part-of-speech assignment to words in unrestricted text. The model has the advantage that a pre-tagged training corpus is not required. Words are represented by equivalence classes to reduce the number of parameters required and provide an essentially vocabulary-independent model. State chains are used to model selective higher-order conditioning in the model, which obviates the proliferation of parameters attendant in uniformly higher-order models. The structure of the state chains is based on both an analysis of errors and linguistic knowledge. Examples show how word dependency across phrases can be modeled.
Year
DOI
Venue
1989
10.3115/1075434.1075451
HLT
Keywords
Field
DocType
higher-order model,part-of-speech assignment,equivalence class,vocabulary-independent model,hidden markov model,state chain,parameters attendant,linguistic knowledge,selective higher-order conditioning,pre-tagged training corpus,phrase-dependent word tagging,unrestricted text,higher order,model selection,part of speech
Computer science,Markov model,Phrase,Speech recognition,Artificial intelligence,Natural language processing,Equivalence class,Hidden Markov model,Hidden semi-Markov model
Conference
ISBN
Citations 
PageRank 
1-55860-112-0
13
72.05
References 
Authors
3
1
Name
Order
Citations
PageRank
Julian Kupiec11061381.10