Label Embedding for Zero-shot Fine-grained Named Entity Typing

By Yukun Ma, Erik Cambria, Sa Gao. 2016

149 citations recorded by the source corpus.

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Citations in the corpus

Recent Trends in Deep Learning Based Natural Language Processing (2017, background): “(Ma et al., 2016) exploited several embeddings, including character trigrams, to incorporate prototypical and hierarchical information for learning pre-trained label embeddings in the context of NER.”
Joint Embedding of Words and Labels for Text Classification (2018, background): “It is particularly successful on the task of zero-shot learning (Palatucci et al., 2009; Yogatama et al., 2015; Ma et al., 2016), where the label correlation captured in the embedding space can improve the prediction when some classes are unseen.”
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (2018, background): “Many works in NLP mainly focus on the zero-shot/semisupervised scenario (Xie et al., 2016; Ma et al., 2016; Carlson et al., 2009), which incorporate ex-”
Learning from Task Descriptions (2020, methodology): “Zero-shot learning has been widely adopted including for classi-ﬁcation (Dauphin et al., 2013), entity typing (Ma et al., 2016; Zhou et al., 2018) and relation extraction (Levy et al., 2017; Shi and Lin, 2019).”
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM (2018, background): “…research problem (Cambria et al. 2017b) that requires tackling many natural language processing (NLP) tasks, including named entity recognition (Ma, Cambria, and Gao 2016), word polarity disambiguation (Xia et al. 2015), personality recognition (Majumder et al. 2017), sarcasm detection (Poria…”
Bayesian network based extreme learning machine for subjectivity detection (2017, background): “While most works approach it as a simple categorization problem, sentiment analysis is actually a ‘suitcase’ research problem that requires tackling many natural language processing (NLP) sub-tasks, including aspect extraction [4], named entity recognition [5], word polarity disambiguation [6], sema”
Natural language based financial forecasting: a survey (2017, background): “Sentiment analysis [16] is a ‘suitcase research problem’ [19] that requires tackling many NLP sub-tasks, including aspect extraction [92], subjectivity detection [27], named entity recognition [73], and sarcasm detection [93], but also complementary tasks such as personality recognition [74], user p”
Context-Dependent Sentiment Analysis in User-Generated Videos (2017, background): “, 2016a), named entity recognition (Ma et al., 2016), concept extraction (Rajagopal et al.”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “[32] exploited several embeddings, including character trigrams, to incorporate proto-”
Multi-Task Label Embedding for Text Classification (2017, background): “The idea of embedding label is not new and is primarily designed to improve general neural text classification models (Bengio et al., 2010; Norouzi et al., 2013; Ma et al., 2016), but here we mainly focus on integrating label embedding to enhance MTL.”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “[94] proposed prototype-driven label embedding with hierarchical information for zero-shot fine-grained named entity typing.”
Unknown Intent Detection Using Gaussian Mixture Model with an Application to Zero-shot Intent Classification (2020, background): “(1) In training, LMCL ignores the prior knowledge of class labels, while it has been shown that label correlations captured in the embedding space can improve prediction performance, especially in the zero-shot learning scenarios (Palatucci et al., 2009; Ma et al., 2016).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “[139] develops Proto-HLE that models the prototype of entity label embeddings for zero-shot fine-grain ET tasks, combining prototypical features with hierarchical type labels for inferring essential features of a new type.”
A Survey of Zero-Shot Learning (2019, methodology): “When learning the classifier, existing methods either treat each class label individually [14, 99, 100, 168] or exploit multilabel correlations [44, 84, 107, 126, 172].”