nlp.statement('She sells seashells').negate().text() // She doesn't sell seashells nlp.sentence('I fed the dog').replace('the [Noun]', 'the cat').text() // I fed the cat nlp.text("Tony Hawk did a kickflip").people(); // [ Person { text: 'Tony Hawk' ..} ] -- https://news.ycombinator.com/item?id=11695904
Stanford CoreNLP provides a set of natural language analysis tools. It can give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, and mark up the structure of sentences in terms of phrases and word dependencies, indicate which noun phrases refer to the same entities, indicate sentiment, extract open-class relations between mentions, etc.
My primary area of research is Arabic Computational Linguistics. Specifically:
Stemming: Details about the stemmer I have developed for Arabic. With link to Java code.
Tagging: Details about the Part-Of-Speech (POS) tagger I am developing for Arabic.
Corpora: Details about the Arabic corpora I am using. I have manually tagged 50,000 words of Arabic newspaper text with the basic tags (noun, verb, particle). I have also tagged 1,700 words with more detailed tags (i.e. singular, masculine, definite common noun). These are available for research purposes. Please e-mail me if you would like a copy of them.
Publications: I have included a couple of my publications here that can be viewed or downloaded.
Over 725 definitions and hundreds of articles on the paranormal, the occult, the supernatural, and the pseudoscientific written from a skeptic's point of view, in defense of reason, science, and critical thinking, and opposing superstition, fraud, deception, and irrationality.
This page contains various evaluation benchmarks developed and released (as open source) by researchers working in the field of semantics (in no particular order). If you know of a resource that should be present here, please drop me an email.
Part-of-Speech Tagging, Phrase Chunking and Named Entity Recognition with Python NLTK. Taggers and chunkers trained on treebank, brown, conll2000, ieer.
G. Skitalinskaya, M. Spliethöver, und H. Wachsmuth. Proceedings of the 16th International Natural Language Generation Conference, Seite 134--152. (2023)DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions..
M. Sengupta. Findings of the Association for Computational Linguistics: EMNLP 2023, Seite 4636–4659. Association for Computational Linguistics (ACL), (Dezember 2023)
Z. Nouri, N. Prakash, U. Gadiraju, und H. Wachsmuth. IUI 2023 - Proceedings of the 28th International Conference on Intelligent User Interfaces, Seite 737–749. United States, Association for Computing Machinery (ACM), (27.03.2023)
G. Lapesa, {. Vecchi, S. Villata, und H. Wachsmuth. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), (Mai 2023)Funding Information: Gabriella Lapesa and Eva Maria Vecchi are funded by the Bundesministerium für Bildung und Forschung (BMBF), project E-DELIB (Powering up E-deliberation: towards AI-supported moderation). Serena Villata is supported by the French government, through the 3IA Côte d’Azur Investments in the Future project managed by the ANR with the reference number ANR-19-P3IA-0002.; 17th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2023 ; Conference date: 02-05-2023 Through 04-05-2023.
G. Faggioli, {. Clarke, G. Demartini, M. Hagen, C. Hauff, N. Kando, E. Kanoulas, M. Potthast, B. Stein, H. Wachsmuth und 1 andere Autor(en). ICTIR '23, Seite 39--50. Association for Computing Machinery, Inc, (09.08.2023)Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. 1846017. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. ; 9th ACM SIGIR International Conference on the Theory of Information Retrieval : ICTIR 2023 ; Conference date: 23-07-2023 Through 23-07-2023.
M. Alshomary, und H. Wachsmuth. EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Seite 957--967. Association for Computational Linguistics (ACL), (2023)Funding Information: This work was funded by the Deutsche Forschungs-gemeinschaft (DFG, German Research Foundation): TRR 318/1 2021 - 438445824. We would also like to thank the reviewers and the participants who took part anonymously in our user study.; 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 ; Conference date: 02-05-2023 Through 06-05-2023.
T. Völker, J. Pfister, T. Koopmann, und A. Hotho. (2024)cite arxiv:2401.09092Comment: Accepted at 2024 ACM SIGIR CHIIR, For a demo see here http://professor-x.de/demos/bibsonomy-chatgpt/demo.mp4.
T. Völker, J. Pfister, T. Koopmann, und A. Hotho. (2024)cite arxiv:2401.09092Comment: Accepted at 2024 ACM SIGIR CHIIR, For a demo see here http://professor-x.de/demos/bibsonomy-chatgpt/demo.mp4.
R. Pryzant, D. Iter, J. Li, Y. Lee, C. Zhu, und M. Zeng. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Seite 7957--7968. Singapore, Association for Computational Linguistics, (Dezember 2023)