7,924 research outputs found
Bipartite Flat-Graph Network for Nested Named Entity Recognition
In this paper, we propose a novel bipartite flat-graph network (BiFlaG) for
nested named entity recognition (NER), which contains two subgraph modules: a
flat NER module for outermost entities and a graph module for all the entities
located in inner layers. Bidirectional LSTM (BiLSTM) and graph convolutional
network (GCN) are adopted to jointly learn flat entities and their inner
dependencies. Different from previous models, which only consider the
unidirectional delivery of information from innermost layers to outer ones (or
outside-to-inside), our model effectively captures the bidirectional
interaction between them. We first use the entities recognized by the flat NER
module to construct an entity graph, which is fed to the next graph module. The
richer representation learned from graph module carries the dependencies of
inner entities and can be exploited to improve outermost entity predictions.
Experimental results on three standard nested NER datasets demonstrate that our
BiFlaG outperforms previous state-of-the-art models.Comment: Accepted by ACL202
Empower Sequence Labeling with Task-Aware Neural Language Model
Linguistic sequence labeling is a general modeling approach that encompasses
a variety of problems, such as part-of-speech tagging and named entity
recognition. Recent advances in neural networks (NNs) make it possible to build
reliable models without handcrafted features. However, in many cases, it is
hard to obtain sufficient annotations to train these models. In this study, we
develop a novel neural framework to extract abundant knowledge hidden in raw
texts to empower the sequence labeling task. Besides word-level knowledge
contained in pre-trained word embeddings, character-aware neural language
models are incorporated to extract character-level knowledge. Transfer learning
techniques are further adopted to mediate different components and guide the
language model towards the key knowledge. Comparing to previous methods, these
task-specific knowledge allows us to adopt a more concise model and conduct
more efficient training. Different from most transfer learning methods, the
proposed framework does not rely on any additional supervision. It extracts
knowledge from self-contained order information of training sequences.
Extensive experiments on benchmark datasets demonstrate the effectiveness of
leveraging character-level knowledge and the efficiency of co-training. For
example, on the CoNLL03 NER task, model training completes in about 6 hours on
a single GPU, reaching F1 score of 91.710.10 without using any extra
annotation.Comment: AAAI 201
Lexicon Infused Phrase Embeddings for Named Entity Resolution
Most state-of-the-art approaches for named-entity recognition (NER) use semi
supervised information in the form of word clusters and lexicons. Recently
neural network-based language models have been explored, as they as a byproduct
generate highly informative vector representations for words, known as word
embeddings. In this paper we present two contributions: a new form of learning
word embeddings that can leverage information from relevant lexicons to improve
the representations, and the first system to use neural word embeddings to
achieve state-of-the-art results on named-entity recognition in both CoNLL and
Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for
CoNLL 2003---significantly better than any previous system trained on public
data, and matching a system employing massive private industrial query-log
data.Comment: Accepted in CoNLL 201
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