In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
Relation extraction on an open-domain knowledge base
Accompanying repository for our EMNLP 2017 paper. It contains the code to replicate the experiments and the pre-trained models for sentence-level relation extraction.
Although term extraction has been researched for more than 20 years, only a few studies focus on under-resourced languages. Moreover, bilingual term mapping from comparable corpora for these languages has attracted researchers only recently. This paper presents methods for term extraction, term tagging in documents, and bilingual term mapping from comparable corpora for four under-resourced languages: Croatian, Latvian, Lithuanian, and Romanian. Methods described in this paper are language independent as long as language specific parameter data is provided by the user and the user has access to a part of speech or a morpho-syntactic tagger.
Text mining and web scraping involves chunk parsing and recognition of named entities (institutions, dates, titles)...The extraction of named entities is mostly based on a strategy that combines look up in gazetteers (lists of companies, cities, etc.) wit
The main task of the GenIELex project is the development of a biochemistry specific lexicon as well as of an annotated corpus for the evaluation of the system. The need for the construction of such a lexicon is illustrated by the following figures, based
Todays feature of the week post will point you to one of the hidden features of the system. As most of you certainly know one way to acquire the meta data of a publication is to use the screen scraping facility of BibSonomy.
M. Schwab, R. Jäschke, and F. Fischer. Proceedings of the 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 110--115. Association for Computational Linguistics, (2023)
F. Arnold, and R. Jäschke. Proceedings of the Workshop Understanding LIterature references in academic full TExt at JCDL 2022, volume 3220 of ULITE-ws '22, page 7--15. CEUR Workshop Proceedings, (2022)
M. Schwab, R. Jäschke, and F. Fischer. Proceedings of the 5th International Conference on Natural Language and Speech Processing, page 282--287. Association for Computational Linguistics, (2022)