We propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using features extracted from a recurrent neural network. In evaluations on three standard data sets, we show that our approach significantly outperforms existing state-of-the-art methods, which are feature-based. The approach is also efficient: it operates linearly in the number of tokens and the number of possible output labels at any token. Finally, we present an extension of our model that jointly learns the head of each entity mention.
We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms featurebased joint model by Li and Ji (2014). We also compare our model with an end-toend tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1% on entity mentions and 2% on relations. Our finegrained analysis also shows that our model performs significantly better on AGENT-ARTIFACT relations, while SPTree performs better on PHYSICAL and PART-WHOLE relations.
We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this featurespace is far more effective than the standard meta-learning approaches. We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training. We show that our method of combining structured decoding with nearest neighbor learning achieves stateof-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by 6% to 16% absolute points over prior metalearning based systems.
We investigate the use of deep bidirectional LSTMs for joint extraction of opinion entities and the IS-FROM and IS-ABOUT relations that connect them-the first such attempt using a deep learning approach. Perhaps surprisingly, we find that standard LSTMs are not competitive with a state-of-the-art CRF+ILP joint inference approach (Yang and Cardie, 2013) to opinion entities extraction, performing below even the standalone sequencetagging CRF. Incorporating sentence-level and a novel relation-level optimization, however, allows the LSTM to identify opinion relations and to perform within 1-3% of the state-of-the-art joint model for opinion entities and the IS-FROM relation; and to perform as well as the state-of-theart for the IS-ABOUT relation-all without access to opinion lexicons, parsers and other preprocessing components required for the feature-rich CRF+ILP approach.
We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this featurespace is far more effective than the standard meta-learning approaches. We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training. We show that our method of combining structured decoding with nearest neighbor learning achieves stateof-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by 6% to 16% absolute points over prior metalearning based systems.
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