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.
Neil Ireson, Fabio Ciravegna, Marie Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli: Evaluating Machine Learning for Information Extraction, 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, 7-11 August, 2005
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
This project aims to develop an efficient rule based extractor of entries of references, located in scientific articles in English language. The application takes a pdf file or a directory of pdf and then returns an html file, containing the list of all entries with their respective title. Moreover the title of the article cited is searched through Google Web Service to get the URL that identifying the article on the web. If the URL provides on the page a Bibtex entry, this will appear in the html output under the relative entries, stolen from some typical site like citeseer, ieeexlpore etc. The application does not make search over pdf file based on images.