Mention Detection

While significant effort has been put into an- notating linguistic resources for several lan- guages, there are still many left that have only small amounts of such resources. This paper …...

AbstractThe Arabic language has a very rich morphology where a word is composed of zero or more prefixes, a stem and zero or more suffixes. This makes Arabic data sparse …...

The research question treated in this paper is centered on the idea of exploiting rich re- sources of one language to enhance the per- formance of a mention detection system …...

Research in named entity recognition and mention detection has typically involved a fairly small number of semantic classes, which may not be adequate if seman- tic class information is intended …...

This paper presents a mixed deterministic model for coreference resolution in the CoNLL-2012 shared task. We separate the two main stages of our model, mention detection and coreference resolution, into …...

The CoNLL-2012 shared task is an extension of the last year’s coreference task. We partici- pated in the closed track of the shared tasks in both years. In this paper, …...

Information extraction from microblog posts is an important task, as today microblogs cap- ture an unprecedented amount of information and provide a view into the pulse of the world. As …...

AbstractIn this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing. Instead of treating NER as a sequence labeling problem, …...

AbstractElazar and Goldberg (2018) showed that protected attributes can be extracted from the representations of a debiased neural network for mention detection at above-chance levels, by evaluating a diagnostic classifier …...

AbstractWe propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED). Our model applies task-specific heads on top of shared BERT contextualized embeddings. We …...

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 …...

AbstractWe point out that common evaluation practices for cross-document coreference resolution have been unrealistically permissive in their assumed settings, yielding inflated results. We propose addressing this issue via two evaluation …...