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Publication Year: 2019

1 to 10 of 297 Results

SRL4ORL_NAACL_2018.zip Feb 4, 2019 - SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code] ZIP Archive - 14.0 MB - MD5: 74133c7d8035fc79da35083cda7fb67d Code
SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code] Feb 4, 2019 Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1 This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar...
Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis" Feb 6, 2019 Heinzerling, Benjamin, 2019, "Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"", https://doi.org/10.11588/data/9JKAVW, heiDATA, V1 This dataset contains source code and system output used in the PhD thesis "Aspects of Coherence for Entity Analysis". This dataset is split into three parts corresponding to the chapters describing the three main contributions of the thesis: chapter3.tar.gz: Java source code for...
Selectional Preference Embeddings (EMNLP 2017) Jan 31, 2019 Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1 Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...
dep.word.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.txt Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017) Plain Text - 819.4 MB - MD5: cfec356deebf49b8069b1f7a0565afd8 selectional preference embeddings in binary word2vec format. unlemmatized, enhancedPlusPlusDependencies.
dep.word.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.bin Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017) Unknown - 356.8 MB - MD5: a03487f77dc5908a9930e0d228579eaf selectional preference embeddings in binary word2vec format. unlemmatized, enhancedPlusPlusDependencies.
dep.word.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.txt Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017) Plain Text - 811.6 MB - MD5: a29ef8d34067f5945726340138e8d1a8 selectional preference embeddings in binary word2vec format. unlemmatized, enhancedDependencies.
dep.word.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.bin Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017) Unknown - 353.3 MB - MD5: a05105844ee4a730714e0747b9713086 selectional preference embeddings in binary word2vec format. unlemmatized, enhancedDependencies.
dep.word.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.txt Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017) Plain Text - 577.6 MB - MD5: 05934936f826113573c1fead5b8f5070 selectional preference embeddings in binary word2vec format. unlemmatized, basicDependencies.
dep.word.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.bin Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017) Unknown - 250.9 MB - MD5: 1247ad86b659ea27f593a594274b094d selectional preference embeddings in binary word2vec format. unlemmatized, basicDependencies.

SRL4ORL_NAACL_2018.zip

Feb 4, 2019 - SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]

ZIP Archive - 14.0 MB -

Code

SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]

Feb 4, 2019

Marasovic, Ana, 2019, "SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning With Semantic Role Labeling [Source Code]", https://doi.org/10.11588/data/LWN9XE, heiDATA, V1

This repository contains code for reproducing experiments done in Marasovic and Frank (2018). Paper abstract: For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question "Who expressed what kind of sentiment towar...

Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"

Feb 6, 2019

Heinzerling, Benjamin, 2019, "Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis"", https://doi.org/10.11588/data/9JKAVW, heiDATA, V1

This dataset contains source code and system output used in the PhD thesis "Aspects of Coherence for Entity Analysis". This dataset is split into three parts corresponding to the chapters describing the three main contributions of the thesis: chapter3.tar.gz: Java source code for...

Selectional Preference Embeddings (EMNLP 2017)

Jan 31, 2019

Heinzerling, Benjamin, 2019, "Selectional Preference Embeddings (EMNLP 2017)", https://doi.org/10.11588/data/FJQ4XL, heiDATA, V1

Joint embeddings of selectional preferences, words, and fine-grained entity types. The vocabulary consists of: verbs and their dependency relation separated by "@", e.g. "sink@nsubj" or "elect@dobj" words and short noun phrases, e.g. "Titanic" fine-grained entity types using the...

dep.word.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.txt

Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017)

Plain Text - 819.4 MB -

selectional preference embeddings in binary word2vec format. unlemmatized, enhancedPlusPlusDependencies.

dep.word.lower.enhancedPlusPlusDependencies.d100.min100.i10.w2v.mu_postproc.bin

Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017)

Unknown - 356.8 MB -

selectional preference embeddings in binary word2vec format. unlemmatized, enhancedPlusPlusDependencies.

dep.word.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.txt

Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017)

Plain Text - 811.6 MB -

selectional preference embeddings in binary word2vec format. unlemmatized, enhancedDependencies.

dep.word.lower.enhancedDependencies.d100.min100.i10.w2v.mu_postproc.bin

Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017)

Unknown - 353.3 MB -

selectional preference embeddings in binary word2vec format. unlemmatized, enhancedDependencies.

dep.word.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.txt

Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017)

Plain Text - 577.6 MB -

selectional preference embeddings in binary word2vec format. unlemmatized, basicDependencies.

dep.word.lower.basicDependencies.d100.min100.i10.w2v.mu_postproc.bin

Jan 31, 2019 - Selectional Preference Embeddings (EMNLP 2017)

Unknown - 250.9 MB -

selectional preference embeddings in binary word2vec format. unlemmatized, basicDependencies.

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