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AIPHES (Heidelberg University, Technical University of Darmstadt, HITS)
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Data publications from the DFG-funded research training group on Adaptive Information Processing from Heterogeneous Sources (AIPHES) at the CS Department at the Technical University of Darmstadt, the Institute for Computational Linguistics at the University of Heidelberg and the NLP Group at HITS.
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1 to 5 of 5 Results
Feb 6, 2019
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...
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...
Feb 4, 2019
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...
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...
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...
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