Head Selection Parsers and LSTM Labelers (doi:10.11588/data/BPWWJL)

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Part 2: Study Description
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Document Description

Citation

Title:

Head Selection Parsers and LSTM Labelers

Identification Number:

doi:10.11588/data/BPWWJL

Distributor:

heiDATA

Date of Distribution:

2023-11-13

Version:

1

Bibliographic Citation:

Do, Bich-Ngoc; Rehbein, Ines; Frank, Anette, 2023, "Head Selection Parsers and LSTM Labelers", https://doi.org/10.11588/data/BPWWJL, heiDATA, V1

Study Description

Citation

Title:

Head Selection Parsers and LSTM Labelers

Identification Number:

doi:10.11588/data/BPWWJL

Authoring Entity:

Do, Bich-Ngoc (Institute of Computational Linguistics, Heidelberg University & Leibniz Institute for German Language)

Rehbein, Ines (Leibniz Institute for German Language)

Frank, Anette (Institute of Computational Linguistics)

Date of Production:

2017

Distributor:

heiDATA

Access Authority:

Do, Bich-Ngoc

Holdings Information:

https://doi.org/10.11588/data/BPWWJL

Study Scope

Keywords:

Arts and Humanities, Computer and Information Science, syntactic parsing, dependency parsing, grammatical function labeling

Topic Classification:

Dependency Parsing

Abstract:

<p>This resource contains code, data and pre-trained models for various types of neural dependency parsers and LSTM labelers used in the papers: <ul> <li>Do et al. (2017). "What Do We Need to Know About an Unknown Word When Parsing German"</li> <li>Do and Rehbein (2017). "Evaluating LSTM Models for Grammatical Function Labelling"</li> </ul> </p> <p>The parsers and labelers are inspired by the head-selection parser of Zhang et al., (2017). We extend the parser to use different input features, namely: <ul> <li>Word embeddings</li> <li>POS tag embeddings</li> <li>Constituent embeddings (e.g., characters or compound)</li> </ul> and their combinations.</p> <p>Grammatical function labeling is formulated as a sequence labeling task. We introduce two new bidirectional LSTMs labelers with different orders of tree nodes (<em>linear</em> and <em>BFS</em> order) and another labeler based on top-down tree LSTMs.</p>

Kind of Data:

source code

Kind of Data:

pre-trained model

Methodology and Processing

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Title:

Bich-Ngoc Do, Ines Rehbein and Anette Frank (2017). "What Do We Need to Know about an Unknown Word When Parsing German". In: <em>Proceedings of the First Workshop on Subword and Character Level Models in NLP</em>. Copenhagen, Denmark: Association for Computational Linguistics, pp. 117–123.

Identification Number:

10.18653/v1/W1 7-4117

Bibliographic Citation:

Bich-Ngoc Do, Ines Rehbein and Anette Frank (2017). "What Do We Need to Know about an Unknown Word When Parsing German". In: <em>Proceedings of the First Workshop on Subword and Character Level Models in NLP</em>. Copenhagen, Denmark: Association for Computational Linguistics, pp. 117–123.

Citation

Title:

Bich-Ngoc Do and Ines Rehbein (2017). "Evaluating LSTM Models for Grammatical Function Labelling". In: <em>Proceedings of the 15th International Conference on Parsing Technologies</em>. Pisa, Italy: Association for Computational Linguistics, pp. 128–133.

Bibliographic Citation:

Bich-Ngoc Do and Ines Rehbein (2017). "Evaluating LSTM Models for Grammatical Function Labelling". In: <em>Proceedings of the 15th International Conference on Parsing Technologies</em>. Pisa, Italy: Association for Computational Linguistics, pp. 128–133.

Other Study-Related Materials

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README.md

Notes:

text/markdown

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bilstm-parser.zip

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