1 to 10 of 112 Results
Becker, Maria, 2024, "CoCo-Ex", https://doi.org/10.11588/data/K8MCIW, heiDATA, V1
CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph. |
Becker, Maria, 2024, "LLMs4Implicit-Knowledge-Generation Public", https://doi.org/10.11588/data/5VTJ26, heiDATA, V1
Code for equipping pretrained language models (BART, GPT-2, XLNet) with commonsense knowledge for generating implicit knowledge statements between two sentences, by (i) finetuning the models on corpora enriched with implicit information; and by (ii) constraining models with key c... |
Feb 26, 2024 - RATIO_EXPLAIN
Becker, Maria, 2024, "CO-NNECT", https://doi.org/10.11588/data/SAJAD3, heiDATA, V1
This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classificatio... |
Feb 26, 2024 - RATIO_EXPLAIN
Becker, Maria, 2024, "IKAT-DE", https://doi.org/10.11588/data/4BA5LY, heiDATA, V1
A corpus consisting of high-quality human annotations of missing and implied information in argumentative texts (German version). The data is further annotated with semantic clause types and commonsense knowledge relations. |
Feb 26, 2024
Open Research Data from the ExpLAIN project, a joint research project of the NLP Group at the Computational Linguistics Department of Heidelberg University and the Data and Web Science Groupat University of Mannheim. |
Becker, Maria, 2024, "IKAT-EN", https://doi.org/10.11588/data/RUBM2E, heiDATA, V1, UNF:6:To3aHa8xO8P28fzpCz1Qvw== [fileUNF]
A corpus consisting of high-quality human annotations of missing and implied information in argumentative texts (English version). The data is further annotated with semantic clause types and commonsense knowledge relations. |
Jan 17, 2024 - Natural Language Processing Group
Opitz, Juri Alexander, 2024, "Source code and data for the PhD Thesis "Metrics of Graph-Based Meaning Representations with Applications from Parsing Evaluation to Explainable NLG Evaluation and Semantic Search"", https://doi.org/10.11588/data/RAS7U7, heiDATA, V1
This dataset contains source code and data used in the PhD thesis "Metrics of Graph-Based Meaning Representations with Applications from Parsing Evaluation to Explainable NLG Evaluation and Semantic Search". The dataset is split into five repositories: S3BERT: Source code to run... |
Jan 17, 2024
The main purpose of language is to encode and communicate information of all sorts. Our research focuses on semantics — the study of meaning — and how a machine can assign meaning to utterances: words, sentences and texts, as humans can do. Our work is linguistically informed and... |
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Fankhauser, Peter; Do, Bich-Ngoc; Kupietz, Marc, 2023, "Neural Dependency Parser with Biaffine Attention", https://doi.org/10.11588/data/DZ9MUS, heiDATA, V1
This resource contains the code of the dependency parser used in the paper: Fankhauser, et al. (2020). "Evaluating a Dependency Parser on DeReKo". The parser is a re-implementation of the neural dependency parser from Dozat and Manning (2017). In addition, we include two pre-trai... |
Nov 13, 2023 - Neural Techniques for German Dependency Parsing
Do, Bich-Ngoc; Rehbein, Ines, 2023, "Datasets for Dependency Tree Reranking", https://doi.org/10.11588/data/E5NOYH, heiDATA, V1
This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are described in the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation". |
