Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media (N18-1)
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| Challenge: | Current approaches to Named Entity Recognition (NER) are effective in formal text, but they fail on informal text, where improper grammatical structures, spelling inconsistencies, and slang vocabulary prevail. |
| Approach: | They propose a multitask end-to-end bidirectional long short-term memory (BLSTM)-Conditional Random Field (CRF) network with two CRF classifiers and a feature extractor that transfers learning to a CRF for prediction. |
| Outcome: | The proposed models outperform the current state-of-the-art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments. |
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| Challenge: | Social media posts often contain inconsistent or incomplete syntax and lexical notations with limited textual contexts. |
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| Challenge: | Existing approaches for named entity recognition suffer from data sparsity problems when conducted on short and informal texts. |
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| Challenge: | Named Entity Recognition (NER) is a longstanding NLP task that consists of identifying an entity in a sentence or document. |
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| Challenge: | Named Entity Recognition (NER) is a widely adopted NLP task . authors present three variants of NER task, with dataset to support them . |
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| Challenge: | Named Entity Recognition (NER) is an important text analysis task . code-mixing occurs when lexical items and grammatical features from two languages appear in one sentence . |
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Robust Lexical Features for Improved Neural Network Named-Entity Recognition (C18-1)
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| Challenge: | Named-Entity Recognition (NER) uses word embeddings to extend, rather than replace, hand-crafted features. |
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Multimodal Named Entity Disambiguation for Noisy Social Media Posts (P18-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
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| Challenge: | Named entity recognition models are challenging for languages with little training data. |
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Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
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