Genre-sensitive Neural Situation Entity classifier (DE, EN) (ICPSR doi:10.11588/data/XXKWU0)

View:

Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
Entire Codebook

Document Description

Citation

Title:

Genre-sensitive Neural Situation Entity classifier (DE, EN)

Identification Number:

doi:10.11588/data/XXKWU0

Distributor:

heiDATA

Date of Distribution:

2019-10-22

Version:

1

Bibliographic Citation:

Becker, Maria, 2019, "Genre-sensitive Neural Situation Entity classifier (DE, EN)", https://doi.org/10.11588/data/XXKWU0, heiDATA, V1

Study Description

Citation

Title:

Genre-sensitive Neural Situation Entity classifier (DE, EN)

Identification Number:

doi:10.11588/data/XXKWU0

Authoring Entity:

Becker, Maria (Department of Computational Linguistics, Heidelberg University, Germany)

Date of Production:

2017

Distributor:

heiDATA

Date of Distribution:

2019-10-22

Study Scope

Keywords:

Arts and Humanities, Computer and Information Science, situation entity, classification, entity types, German, English

Topic Classification:

semantic modeling

Abstract:

<p>This is a Classifier for situation entity types as described in Becker et al., 2017. These clause types depend on a combination of syntactic-semantic and contextual features. We explore this task in a deeplearning framework, where tuned word representations capture lexical, syntactic and semantic features. We introduce an attention mechanism that pinpoints relevant context not only for the current instance, but also for the larger context. The advantage of our neural model is that it avoids the need to reproduce linguistic features for other languages and is thus more easily transferable.</p> <p>We provide code for the basic local model (GRU), the local model with attention (GRU+attention), and our best performing context model which uses labels of previous clauses and genre information (GRU+attention+label+genre).</p> <p>The data we used for our experiments can be found here, and we used the same train-dev-test split: <a href="https://github.com/annefried/sitent/tree/master/annotated_corpus ">https://github.com/annefried/sitent/tree/master/annotated_corpus </a></p>

Kind of Data:

program source code, python scripts

Methodology and Processing

Other Study-Related Materials

Label:

RNN_for_Clause_Classification-master.zip

Notes:

application/zip