Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

By K. T. Maxwell, Jon Oberlander, W. Bruce Croft. 2013

312 citations recorded by the source corpus.

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Citations in the corpus

Recent Trends in Deep Learning Based Natural Language Processing (2017, methodology): “For example, [13] used embeddings along with stacked denoising autoencoders for domain adaptation in sentiment classification and [14] presented combinatory categorial autoencoders to learn the compositionality of sentence.”
Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks (2015, background): “For example, (Xie et al., 2013) has proposed to use semantic frame parsers to generalize from sentences to scenarios to detect the roles of specific companies (positive or negative), where support vector machines with tree kernels are used as predictive models.”
A Primer on Neural Network Models for Natural Language Processing (2015, neutral): “…on parse trees (Iyyer, Enns, Boyd-Graber, & Resnik, 2014b), sentiment classification (Socher, Perelygin, Wu, Chuang, Manning, Ng, & Potts, 2013; Hermann & Blunsom, 2013), target-dependent sentiment classification (Dong, Wei, Tan, Tang, Zhou, & Xu, 2014) and question answering (Iyyer,…”
Computational Sociolinguistics: A Survey (2016, methodology): “Early computational studies on social identity and language use were based on formal texts, such as the British National Corpus (Argamon et al. 2003; Koppel, Argamon, and Shimoni 2002), or datasets collected from controlled settings, such as recorded conversations (Singh 2001) and telephone conversa”
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models (2023, methodology): “A rule-based metric that is easy to implement, the Jaccard index has been extensively utilized in Natural Language Processing (NLP) tasks (Cronin et al., 2017; Pilehvar et al., 2013; Qurashi et al., 2020), where sentences or documents are treated as sets of words.”
Structured Prediction as Translation between Augmented Natural Languages (2021, methodology): “We use the same split as previous work (Ji & Grishman, 2008; Li et al., 2013) with 529 documents for training, 30 for validation, and 40 for testing.”
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input (2018, methodology): “We use the Visual Attributes for Concepts Dataset (VisA) of Silberer et al. (2013), which contains human-generated per-concept attribute annotations for 500 concrete concepts (e.g., cat , sofa , car ) spanning across different categories (e.g., mammals , furniture , vehicles ), annotated with 636…”
Recent Advances in Natural Language Processing via Large Pre-trained Language Models: A Survey (2021, methodology): “Examples are constituency parsing [215], semantic graph parsing using Universal Conceptual Cognitive Annotation ( UCCA ) [1, 73] and Abstract Meaning Representation ( AMR ) [8, 123, 212, 224], Aspect-Based Sentiment Analysis [101], and Machine Translation ( MT ) [226].”
On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, background): “The composition function can be a single-layer neural network or more sophisticated compositions, and different options have been explored in various tasks [30, 308-312].”
In Defense of Word Embedding for Generic Text Representation (2015, result): “We compare our results to the state of the art results [25, 3, 34, 12, 26].”
Recent advances in document summarization (2017, background): “1) Supervised weights [53,97], learning with external resources [100], non-bigram concepts [167]”