Named entity recognition (NER) is the task of tagging entities in text with their corresponding type. Approaches typically use BIO notation, which differentiates the beginning (B) and the inside (I) of entities. O is used for non-entity tokens.
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We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.
Ranked #1 on Question Answering on CoQA
COMMON SENSE REASONING CONVERSATIONAL RESPONSE SELECTION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SENTENCE CLASSIFICATION SENTIMENT ANALYSIS
We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
Ranked #1 on CCG Supertagging on CCGBank
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.
Ranked #1 on Question Answering on SQuAD1.1 dev
COMMON SENSE REASONING COREFERENCE RESOLUTION LINGUISTIC ACCEPTABILITY NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION
We show that the use of web crawled data is preferable to the use of Wikipedia data.
Ranked #1 on Dependency Parsing on Spoken Corpus
Current state-of-the-art approaches for named entity recognition (NER) using BERT-style transformers typically use one of two different approaches: (1) The first fine-tunes the transformer itself on the NER task and adds only a simple linear layer for word-level predictions.
Ranked #1 on Named Entity Recognition on CoNLL03
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
Ranked #2 on Citation Intent Classification on ACL-ARC (using extra training data)
CITATION INTENT CLASSIFICATION CONVERSATIONAL RESPONSE SELECTION COREFERENCE RESOLUTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC ROLE LABELING SENTIMENT ANALYSIS
Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.
Ranked #27 on Named Entity Recognition on CoNLL 2003 (English)
State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available.
Ranked #6 on Named Entity Recognition on CoNLL++
Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high performance.
Ranked #17 on Named Entity Recognition on Ontonotes v5 (English)
We make all code and pre-trained models available to the research community for use and reproduction.