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Research output per year
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.
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 language | English |
|---|---|
| Media of output | Online |
| Publication status | Published - 2010 |
| Externally published | Yes |
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Dive into the research topics of 'Sequor'. Together they form a unique fingerprint.Research output
- 1 Conference contribution
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A Named Entity Labeler for German: exploiting Wikipedia and distributional clusters
Chrupala, G. & Klakow, D., 2010, Proceedings of the Conference on International Language Resources and Evaluation (LREC). p. 552-556 5 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review