1,842 research outputs found
Neural Reranking for Named Entity Recognition
We propose a neural reranking system for named entity recognition (NER). The
basic idea is to leverage recurrent neural network models to learn
sentence-level patterns that involve named entity mentions. In particular,
given an output sentence produced by a baseline NER model, we replace all
entity mentions, such as \textit{Barack Obama}, into their entity types, such
as \textit{PER}. The resulting sentence patterns contain direct output
information, yet is less sparse without specific named entities. For example,
"PER was born in LOC" can be such a pattern. LSTM and CNN structures are
utilised for learning deep representations of such sentences for reranking.
Results show that our system can significantly improve the NER accuracies over
two different baselines, giving the best reported results on a standard
benchmark.Comment: Accepted as regular paper by RANLP 201
Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition
Traditional language models are unable to efficiently model entity names
observed in text. All but the most popular named entities appear infrequently
in text providing insufficient context. Recent efforts have recognized that
context can be generalized between entity names that share the same type (e.g.,
\emph{person} or \emph{location}) and have equipped language models with access
to an external knowledge base (KB). Our Knowledge-Augmented Language Model
(KALM) continues this line of work by augmenting a traditional model with a KB.
Unlike previous methods, however, we train with an end-to-end predictive
objective optimizing the perplexity of text. We do not require any additional
information such as named entity tags. In addition to improving language
modeling performance, KALM learns to recognize named entities in an entirely
unsupervised way by using entity type information latent in the model. On a
Named Entity Recognition (NER) task, KALM achieves performance comparable with
state-of-the-art supervised models. Our work demonstrates that named entities
(and possibly other types of world knowledge) can be modeled successfully using
predictive learning and training on large corpora of text without any
additional information.Comment: NAACL 2019; updated to cite Zhou et al. (2018) EMNLP as a piece of
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