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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 Hits 1 – 20 of 79 1 Wissensrohstoff Text : Eine Einführung in das Text Mining Biemann, Chris (VerfasserIn); Heyer, Gerhard (VerfasserIn). - 2., wesentl. überarb. Auflage 2022. - Wiesbaden : Springer Fachmedien Wiesbaden GmbH, 2022 IDS Mannheim Show details 2 Language Models Explain Word Reading Times Better Than Empirical Predictability ... Hofmann, Markus J.; Remus, Steffen; Biemann, Chris. - : arXiv, 2022 BASE Show details 3 SCoT: Sense Clustering over Time: a tool for the analysis of lexical change ... Haase, Christian; Anwar, Saba; Yimam, Seid Muhie. - : arXiv, 2022 BASE Show details 4 Language Models Explain Word Reading Times Better Than Empirical Predictability Hofmann, Markus J.; Remus, Steffen; Biemann, Chris... In: Front Artif Intell (2022) BASE Show details 5 Probing Pre-trained Language Models for Semantic Attributes and their Values ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Beloucif, Meriem; Biemann, Chris. - : Underline Science Inc., 2021 Abstract: Pretrained Language Models (PTLMs) yield state-of-the-art performance on many Natural Language Processing tasks, including syntax, semantics and commonsense reasoning. In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g. the relation between rich and high net worth. We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values. Such inferences based on seman- tics are intuitive for us humans as part of our language understanding. Since PTLMs are trained on large amount of Wikipedia data, we would assume that they can generate similar predictions. However, our findings reveal that PTLMs perform still much worse than humans on this task. We show an analysis which ex- plains how to exploit our methodology to inte- grate better context and semantics into PTLMs using knowledge bases. ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network URL: https://dx.doi.org/10.48448/h0z0-p829 https://underline.io/lecture/39952-probing-pre-trained-language-models-for-semantic-attributes-and-their-values BASE Hide details 6 WebAnno-MM: EXMARaLDA meets WebAnno Remus, Steffen [Verfasser]; Hedeland, Hanna [Verfasser]; Ferger, Anne [Verfasser]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2020 DNB Subject Category Language Show details 7 Comparison of Different Lexical Resources With Respect to the Tip-of-the-Tongue Problem Zock, Michael; Biemann, Chris In: ISSN: 1598-2327 ; EISSN: 1976-6939 ; Journal of Cognitive Science ; https://hal.archives-ouvertes.fr/hal-03168850 ; Journal of Cognitive Science, Institute for Cognitive Science, Seoul National University, 2020, 21 (2), pp.193-252. &#x27E8;10.17791/jcs.2020.21.2.193&#x27E9; (2020) BASE Show details 8 Introducing various Semantic Models for Amharic: Experimentation and Evaluation with multiple Tasks and Datasets ... Yimam, Seid Muhie; Ayele, Abinew Ali; Venkatesh, Gopalakrishnan. - : arXiv, 2020 BASE Show details 9 Word Sense Disambiguation for 158 Languages using Word Embeddings Only ... Logacheva, Varvara; Teslenko, Denis; Shelmanov, Artem. - : arXiv, 2020 BASE Show details 10 Individual corpora predict fast memory retrieval during reading ... The 28th International Conference on Computational Linguistics 2020; Biemann, Chris; Hofmann, Markus J.. - : Underline Science Inc., 2020 BASE Show details 11 Individual corpora predict fast memory retrieval during reading ... Hofmann, Markus J.; Müller, Lara; Rölke, Andre. - : arXiv, 2020 BASE Show details 12 Token-based spelling variant detection in Middle Low German texts [<Journal>] Barteld, Fabian [Verfasser]; Biemann, Chris [Verfasser]; Zinsmeister, Heike [Verfasser] DNB Subject Category Language Show details 13 Unsupervised Induction of Domain Dependency Graphs - Extracting, Understanding and Visualizing Domain Knowledge Kohail, Sarah [Verfasser]; Biemann, Chris [Akademischer Betreuer]. - Hamburg : Staats- und Universitätsbibliothek Hamburg, 2019 DNB Subject Category Language Show details 14 Making Fast Graph-based Algorithms with Graph Metric Embeddings ... Kutuzov, Andrey; Dorgham, Mohammad; Oliynyk, Oleksiy. - : arXiv, 2019 BASE Show details 15 On the Compositionality Prediction of Noun Phrases using Poincaré Embeddings ... Jana, Abhik; Puzyrev, Dmitry; Panchenko, Alexander. - : arXiv, 2019 BASE Show details 16 Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings ... Aly, Rami; Acharya, Shantanu; Ossa, Alexander. - : arXiv, 2019 BASE Show details 17 Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ... Ustalov, Dmitry; Panchenko, Alexander; Biemann, Chris. - : Zenodo, 2019 BASE Show details 18 Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction ... Ustalov, Dmitry; Panchenko, Alexander; Biemann, Chris. - : Zenodo, 2019 BASE Show details 19 Adaptive Approaches to Natural Language Processing in Annotation and Application ; Adaptive Ansätze zur Verarbeitung natürlicher Sprache in Annotation und Anwendung Yimam, Seid Muhie. - : Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky, 2019 BASE Show details 20 HHMM at SemEval-2019 Task 2: Unsupervised frame induction using contextualized word embeddings Arefyev, Nikolay; Panchenko, Alexander; Anwar, Saba. - : Association for Computational Linguistics, ACL, 2019 BASE Show details Page: 1 2 3 4

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