CoNLL

Statistical named entity recognisers require costly hand-labelled training data and, as a result, most existing corpora are small. We exploit Wikipedia to create a massive corpus of named entity annotated …...

Named entity recognition (NER) for En- glish typically involves one of three gold standards: MUC, CoNLL, or BBN, all created by costly manual annotation. Recent work has used Wikipedia to …...

This paper presents a system based on Finite State Technology that recognises and classifies numerical entities in texts written in Basque. The system deals with a wide range of entities, …...

The possibility of deleting a word from a sen- tence without violating its syntactic correct- ness belongs to traditionally known manifes- tations of syntactic dependency. We introduce a novel unsupervised …...

AbstractEntity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, …...

AbstractThis paper presents a collaborative partitioning algorithm—a novel ensemble-based approach to coreference resolution. Starting from the all-singleton partition, we search for a solution close to the ensemble’s outputs in terms …...

AbstractNoise is inherent in real world datasets and modeling noise is critical during training as it is effective in regularization. Recently, novel semi-supervised deep learning techniques have demonstrated tremendous potential …...

AbstractMention detection is an important preprocessing step for annotation and interpretation in applications such as NER and coreference resolution, but few stand-alone neural models have been proposed able to handle …...