Named entity recognition often fails in idiosyncratic domains. That causes a problem for depending tasks, such as entity linking and relation extraction. We propose a generic and robust approach for high-recall named entity recognition. Our approach is easy to train and offers strong generalization over diverse domain-specific language, such as news documents (e.g. Reuters) or biomedical text (e.g. Medline). Our approach is based on deep contextual sequence learning and utilizes stacked bidirectional LSTM networks. Our model is trained with only few hundred labeled sentences and does not rely on further external knowledge. We report from our results F1 scores in the range of 84-94 on standard datasets.
The task of NER has been extensively studied with various evaluation in the last decades: MUC-6, MUC-7, CoNLL2002, CoNLL2003 and ACE. The standard approach to NER is the application of discriminative tagging @cite_15 to the task of NER @cite_7 , often with linear chain Conditional Random Field (CRF), Hidden Markov (HMM) or Maximum Entropy Hidden Markov Models (MEMM). Later, used continuous-space language models, where type-to-vector word mappings can be learned using backpropagation. achieved a more effective vector representation using the skip-gram model. The model optimizes the likelihood of tokens over a window surrounding a given token. This training process produces a linear classifier that predicts words conditioned on the central token's vector representation.
@cite_15 @cite_7
2008652694 2141099517
We describe new algorithms for training tagging models, as an alternative to maximum-entropy models or conditional random fields (CRFs). The algorithms rely on Viterbi decoding of training examples, combined with simple additive updates. We describe theory justifying the algorithms through a modification of the proof of convergence of the perceptron algorithm for classification problems. We give experimental results on part-of-speech tagging and base noun phrase chunking, in both cases showing improvements over results for a maximum-entropy tagger. Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; , 1998).