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Proceedings ArticleDOI

Distributional part-of-speech tagging

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TLDR
The authors presented an algorithm for tagging words whose part-of-speech properties are unknown, which categorizes word tokens in context instead of word types, and evaluated on the Brown Corpus.
Abstract
This paper presents an algorithm for tagging words whose part-of-speech properties are unknown. Unlike previous work, the algorithm categorizes word tokens in context instead of word types. The algorithm is evaluated on the Brown Corpus.

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Journal Article

Natural Language Processing (Almost) from Scratch

TL;DR: A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.
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Proceedings ArticleDOI

Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency

TL;DR: This work presents a generative model for the unsupervised learning of dependency structures and describes the multiplicative combination of this dependency model with a model of linear constituency that works and is robust cross-linguistically.
Patent

Method and apparatus for building an intelligent automated assistant

TL;DR: In this paper, a method for building an automated assistant includes interfacing a service-oriented architecture that includes a plurality of remote services to an active ontology, where the active ontologies includes at least one active processing element that models a domain.
References
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Journal ArticleDOI

Finding Structure in Time

TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
Journal ArticleDOI

Scatter/Gather: a cluster-based approach to browsing large document collections

TL;DR: A document browsing technique that employs docum-ent clustering as its primary operation is presented and a fast (linear time) clustering algorithm is presented that provides a powerful new access paradigm.
Book ChapterDOI

Parsing By Chunks

TL;DR: The typical chunk consists of a single content word surrounded by a constellation of function words, matching a fixed template, and the relationships between chunks are mediated more by lexical selection than by rigid templates.
Proceedings ArticleDOI

A stochastic parts program and noun phrase parser for unrestricted text

TL;DR: A program that tags each word in an input sentence with the most likely part of speech has been written and performance is encouraging; a 400-word sample is presented and is judged to be 99.5% correct.
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