54 citations · 84 across the 18 of their papers we have counts for
7 papers · 1 filter
Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
Hanchu Zhang, Leonhard Hennig, Christoph Alt +3
Named Entity Recognition (NER) in domains like e-commerce is an understudied problem due to the lack of annotated datasets. Recognizing novel entity types in this domain, such as p…
TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task
Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig
TACRED (Zhang et al., 2017) is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training a…
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig
Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, cond…
SIA: A Scalable Interoperable Annotation Server for Biomedical Named Entities
Johannes Kirschnick, Philippe Thomas, Roland Roller +1
Recent years showed a strong increase in biomedical sciences and an inherent increase in publication volume. Extraction of specific information from these sources requires highly s…
A Corpus Study and Annotation Schema for Named Entity Recognition and Relation Extraction of Business Products
Saskia Schön, Veselina Mironova, Aleksandra Gabryszak +1
Recognizing non-standard entity types and relations, such as B2B products, product classes and their producers, in news and forum texts is important in application areas such as su…
A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events
Martin Schiersch, Veselina Mironova, Maximilian Schmitt +3
Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific c…
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling
Dmitrii Aksenov, Julián Moreno-Schneider, Peter Bourgonje +3
We explore to what extent knowledge about the pre-trained language model that is used is beneficial for the task of abstractive summarization. To this end, we experiment with condi…