Neural Architectures for Named Entity Recognition

By Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, Chris Dyer. 2016

4,356 citations recorded by the source corpus.

Comunicacio presentada a la 2016 Conference of the North American Chapter of the Association for Computational Linguistics, celebrada a San Diego (CA, EUA) els dies 12 a 17 de juny 2016.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Structured Attention Networks (2017, background): “…applications, most notably in seminal work on graph transformers (LeCun et al., 1998), key work on NLP (Collobert et al., 2011), and in many other areas (Peng et al., 2009; Do & Arti´eres, 2010; Jaderberg et al., 2014; Chen et al., 2015; Durrett & Klein, 2015; Lample et al., 2016, inter alia ).”
Semi-Supervised Sequence Modeling with Cross-View Training (2018, methodology): “Our models use two layer CNN-BiLSTM encoders (Chiu and Nichols, 2016; Ma and Hovy, 2016; Lample et al., 2016) and task-speciﬁc prediction modules.”
Deep Contextualized Word Representations (2018, methodology): “Following recent state-of-the-art systems (Lample et al., 2016; Peters et al., 2017), the base-line model uses pre-trained word embeddings, a character-based CNN representation, two biLSTM layers and a conditional random ﬁeld (CRF) loss (Lafferty et al., 2001), similar to Collobert et al. (2011).”
Semi-supervised sequence tagging with bidirectional language models (2017, methodology): “Our baseline sequence tagging model is a hierarchical neural tagging model, closely following a number of recent studies (Ma and Hovy, 2016; Lample et al., 2016; Yang et al., 2017; Chiu and Nichols, 2016) (left side of Figure 2).”
context2vec: Learning Generic Context Embedding with Bidirectional LSTM (2016, methodology): “More recently, bidirectional recurrent neural networks, and speciﬁcally bidirectional LSTMs, were used in such tasks to learn internal representations of wider sentential contexts (Zhou and Xu, 2015; Lample et al., 2016).”
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks (2019, methodology): “For NER, we use a BiLSTM with a CRF layer (Lafferty et al., 2001; Lample et al., 2016).”
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning (2018, background): “…Zisserman, 2014) to extract representations of the image, while several natural language tasks such as reading comprehension and sequence labeling (Lample et al., 2016) have benefited from pretrained word embeddings (Mikolov et al., 2013; Pennington et al., 2014) that are either fine-tuned for a…”
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts (2021, methodology): “Factual knowledge is traditionally extracted from large corpora using a pipeline of NLP tools (Surdeanu and Ji, 2014), including entity extraction (Lample et al., 2016), entity linking (Rao et al., 2013) and relation extraction (Sorokin and Gurevych, 2017).”
Unsupervised Cross-lingual Representation Learning at Scale (2019, methodology): “We consider the LSTM + CRF approach from Lample et al. (2016) and the Flair model from Akbik et al. (2018) CoNLL-2002 and CoNLL-2003 (F1 score).”
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods (2018, methodology): “First, we anonymize named entities using an automatic named entity ﬁnder (Lample et al., 2016).”
Making Neural QA as Simple as Possible but not Simpler (2017, background): “Lample et al. (2016) demonstrated that BiRNNs are powerful at recognizing named entities which makes them sensible choice for context encoding to allow for improved type matching.”
Latent Predictor Networks for Code Generation (2016, methodology): “Finally, HMMs have been employed in neural models to marginalize over label sequences in (Collobert et al., 2011; Lample et al., 2016) by modeling transitions between labels.”
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech (2018, methodology): “The embeddings are obtained via unsupervised learning from co-occurrence information in text, and contain semantic information about the word which are useful for many NLP tasks [4, 5, 6, 7, 8].”
When and Why Are Pre-Trained Word Embeddings Useful for Neural Machine Translation? (2018, methodology): “Pre-trained word embeddings have proven to be highly useful in neural network models for NLP tasks such as sequence tagging (Lample et al., 2016; Ma and Hovy, 2016) and text classification (Kim, 2014).”
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks (2017, methodology): “On the other hand, Lample et al. (2016) use bidirectional LSTMs to derive the character-based representation.”
Sentence-State LSTM for Text Representation (2018, neutral): “Our BiLSTM results are comparable to the results reported by Ma and Hovy (2016) and Lample et al. (2016), who also use bidirectional RNNCRF structures.”
Attention-based LSTM for Aspect-level Sentiment Classification (2016, background): “Neural networks have achieved state-of-the-art performance in a variety of NLP tasks such as machine translation (Lample et al., 2016), paraphrase identiﬁcation (Yin et al., 2015), question answering (Golub and He, 2016) and text summarization (Rush et al., 2015).”