Sequor

Research output: Non-textual formSoftwareOther research output

Abstract

Sequor is a sequence labeler based on Collins's (2002) perceptron. Sequor has a flexible feature template language and is meant mainly for NLP applications such as Named Entity labeling, Part of Speech tagging or syntactic chunking. It includes the SemiNER named entity recognizer, with pre-trained models for German and English (see Named Entity Recognition (SemiNER)).
Sequor is especially useful if your dataset has a large label set. In this case it is likely to run faster and allow you to use much less RAM than a sequence labeler based on Conditional Random Fields. Additionally sequor implements options which allow you to control the size of model and tradeoff speed against accuracy:
size of the beam
label dictionary
feature hashing
See https://bitbucket.org/gchrupala/sequor/wiki/Options for details.
Original languageEnglish
Media of outputOnline
Publication statusPublished - 2010
Externally publishedYes

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Random access storage
Syntactics
Labeling
Labels
Neural networks

Cite this

@misc{be2851b600884b7799376a1968ac43e2,
title = "Sequor",
abstract = "Sequor is a sequence labeler based on Collins's (2002) perceptron. Sequor has a flexible feature template language and is meant mainly for NLP applications such as Named Entity labeling, Part of Speech tagging or syntactic chunking. It includes the SemiNER named entity recognizer, with pre-trained models for German and English (see Named Entity Recognition (SemiNER)).Sequor is especially useful if your dataset has a large label set. In this case it is likely to run faster and allow you to use much less RAM than a sequence labeler based on Conditional Random Fields. Additionally sequor implements options which allow you to control the size of model and tradeoff speed against accuracy:size of the beamlabel dictionaryfeature hashingSee https://bitbucket.org/gchrupala/sequor/wiki/Options for details.",
author = "Grzegorz Chrupala",
year = "2010",
language = "English",

}

Chrupala, G, Sequor, 2010, Software.
Sequor. Chrupala, Grzegorz (Author). 2010.

Research output: Non-textual formSoftwareOther research output

TY - ADVS

T1 - Sequor

AU - Chrupala, Grzegorz

PY - 2010

Y1 - 2010

N2 - Sequor is a sequence labeler based on Collins's (2002) perceptron. Sequor has a flexible feature template language and is meant mainly for NLP applications such as Named Entity labeling, Part of Speech tagging or syntactic chunking. It includes the SemiNER named entity recognizer, with pre-trained models for German and English (see Named Entity Recognition (SemiNER)).Sequor is especially useful if your dataset has a large label set. In this case it is likely to run faster and allow you to use much less RAM than a sequence labeler based on Conditional Random Fields. Additionally sequor implements options which allow you to control the size of model and tradeoff speed against accuracy:size of the beamlabel dictionaryfeature hashingSee https://bitbucket.org/gchrupala/sequor/wiki/Options for details.

AB - Sequor is a sequence labeler based on Collins's (2002) perceptron. Sequor has a flexible feature template language and is meant mainly for NLP applications such as Named Entity labeling, Part of Speech tagging or syntactic chunking. It includes the SemiNER named entity recognizer, with pre-trained models for German and English (see Named Entity Recognition (SemiNER)).Sequor is especially useful if your dataset has a large label set. In this case it is likely to run faster and allow you to use much less RAM than a sequence labeler based on Conditional Random Fields. Additionally sequor implements options which allow you to control the size of model and tradeoff speed against accuracy:size of the beamlabel dictionaryfeature hashingSee https://bitbucket.org/gchrupala/sequor/wiki/Options for details.

M3 - Software

ER -