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1 to 10 of 11 Results

WikiWarsDE Corpus Aug 13, 2014 - Database Systems Research Group Strötgen, Jannik; Gertz, Michael, 2014, "WikiWarsDE Corpus", https://doi.org/10.11588/data/10026, heiDATA, V1 The WikiWarsDE corpus is a German corpus containing Wikipedia articles with annotations of temporal expressions. Its creation was motivated by the English WikiWars corpus (Mazur & Dale 2010). WikiWarsDE was developed to support research on temporal information extraction and norm...
Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016 Nov 2, 2016 - Perspektive Bibliothek Drees, Bastian, 2016, "Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016", https://doi.org/10.11588/data/10090, heiDATA, V2 Es wurden die auf den Homepages angegebenen Ansprechpartner wissenschaftlicher Repositorien und Publikationsserver in Deutschland zu ihren Erfahrungen mit Text und Data Mining befragt. Die Befragung fand zwischen dem 22. und 26.2.2016 per E-Mail statt. Es wurden Ansprechpartner v...
Source Code, Data and Additional Material for the Thesis: "Aspects of Coherence for Entity Analysis" Feb 6, 2019 - AIPHES 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 - AIPHES 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...
PatTR: Patent Translation Resource Jun 16, 2014 - Statistical Natural Language Processing Group Wäschle, Katharina; Riezler, Stefan, 2014, "PatTR: Patent Translation Resource", https://doi.org/10.11588/data/10002, heiDATA, V3 PatTR is a sentence-parallel corpus extracted from the MAREC patent collection. The current version contains more than 22 million German-English and 18 million French-English parallel sentences collected from all patent text sections as well as 5 million German-French sentence pa...
Opinion role extractor Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1 System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text
LibriVoxDeEn - A Corpus for German-to-English Speech Translation and Speech Recognition Jun 13, 2020 - Statistical Natural Language Processing Group Beilharz, Benjamin; Sun, Xin, 2019, "LibriVoxDeEn - A Corpus for German-to-English Speech Translation and Speech Recognition", https://doi.org/10.11588/data/TMEDTX, heiDATA, V2 This dataset is a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The corpus consists of over 100 hours of audio material and over 50k parallel sentences. The speech data are low in disfluencies because of the...
Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material] Apr 24, 2024 - AIPHES Mihaylov, Todor, 2024, "Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]", https://doi.org/10.11588/data/HU3ARF, heiDATA, V1 Machine Reading Comprehension is a language understanding task where a system is expected to read a given passage of text and typically answer questions about it. When humans assess the task of reading comprehension, in addition to the presented text, they usually use the knowled...
BPEmb: Pre-trained Subword Embeddings in 275 Languages (LREC 2018) Feb 6, 2019 - AIPHES Heinzerling, Benjamin, 2019, "BPEmb: Pre-trained Subword Embeddings in 275 Languages (LREC 2018)", https://doi.org/10.11588/data/V9CXPR, heiDATA, V1 BPEmb is a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE). In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages better than alternative subword approaches, while r...
Abstract Anaphora Resolution [Source Code] Feb 4, 2019 - AIPHES Marasovic, Ana, 2019, "Abstract Anaphora Resolution [Source Code]", https://doi.org/10.11588/data/UDMPY5, heiDATA, V1 Abstract Anaphora Resolution (AAR) aims to find the interpretation of nominal expressions (e.g., this result, those two actions) and pronominal expressions (e.g., this, that, it) that refer to abstract-object-antecedents such as facts, events, plans, actions, or situations. The f...

WikiWarsDE Corpus

Aug 13, 2014 - Database Systems Research Group

Strötgen, Jannik; Gertz, Michael, 2014, "WikiWarsDE Corpus", https://doi.org/10.11588/data/10026, heiDATA, V1

The WikiWarsDE corpus is a German corpus containing Wikipedia articles with annotations of temporal expressions. Its creation was motivated by the English WikiWars corpus (Mazur & Dale 2010). WikiWarsDE was developed to support research on temporal information extraction and norm...

Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016

Nov 2, 2016 - Perspektive Bibliothek

Drees, Bastian, 2016, "Text und Data Mining an wissenschaftlichen Repositorien und Publikationsservern in Deutschland - Zusammenfassung der Ergebnisse einer Umfrage im Februar und März 2016", https://doi.org/10.11588/data/10090, heiDATA, V2

Es wurden die auf den Homepages angegebenen Ansprechpartner wissenschaftlicher Repositorien und Publikationsserver in Deutschland zu ihren Erfahrungen mit Text und Data Mining befragt. Die Befragung fand zwischen dem 22. und 26.2.2016 per E-Mail statt. Es wurden Ansprechpartner v...

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

Feb 6, 2019 - AIPHES

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 - AIPHES

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...

PatTR: Patent Translation Resource

Jun 16, 2014 - Statistical Natural Language Processing Group

Wäschle, Katharina; Riezler, Stefan, 2014, "PatTR: Patent Translation Resource", https://doi.org/10.11588/data/10002, heiDATA, V3

PatTR is a sentence-parallel corpus extracted from the MAREC patent collection. The current version contains more than 22 million German-English and 18 million French-English parallel sentences collected from all patent text sections as well as 5 million German-French sentence pa...

Opinion role extractor

Sep 2, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Wiegand, Michael, 2019, "Opinion role extractor", https://doi.org/10.11588/data/3W7AQP, heiDATA, V1

System for the Extraction of Subjective Expressions, Sentiment Sources and Sentiment Targets from German Text

LibriVoxDeEn - A Corpus for German-to-English Speech Translation and Speech Recognition

Jun 13, 2020 - Statistical Natural Language Processing Group

Beilharz, Benjamin; Sun, Xin, 2019, "LibriVoxDeEn - A Corpus for German-to-English Speech Translation and Speech Recognition", https://doi.org/10.11588/data/TMEDTX, heiDATA, V2

This dataset is a corpus of sentence-aligned triples of German audio, German text, and English translation, based on German audio books. The corpus consists of over 100 hours of audio material and over 50k parallel sentences. The speech data are low in disfluencies because of the...

Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]

Apr 24, 2024 - AIPHES

Mihaylov, Todor, 2024, "Knowledge-Enhanced Neural Networks for Machine Reading Comprehension [Source Code and Additional Material]", https://doi.org/10.11588/data/HU3ARF, heiDATA, V1

Machine Reading Comprehension is a language understanding task where a system is expected to read a given passage of text and typically answer questions about it. When humans assess the task of reading comprehension, in addition to the presented text, they usually use the knowled...

BPEmb: Pre-trained Subword Embeddings in 275 Languages (LREC 2018)

Feb 6, 2019 - AIPHES

Heinzerling, Benjamin, 2019, "BPEmb: Pre-trained Subword Embeddings in 275 Languages (LREC 2018)", https://doi.org/10.11588/data/V9CXPR, heiDATA, V1

BPEmb is a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE). In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages better than alternative subword approaches, while r...

Abstract Anaphora Resolution [Source Code]

Feb 4, 2019 - AIPHES

Marasovic, Ana, 2019, "Abstract Anaphora Resolution [Source Code]", https://doi.org/10.11588/data/UDMPY5, heiDATA, V1

Abstract Anaphora Resolution (AAR) aims to find the interpretation of nominal expressions (e.g., this result, those two actions) and pronominal expressions (e.g., this, that, it) that refer to abstract-object-antecedents such as facts, events, plans, actions, or situations. The f...

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