Foundations of Statistical Natural Language Processing

By P. Kantor. 2001

6,195 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

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Citations in the corpus

Plug and Play Language Models: A Simple Approach to Controlled Text Generation (2019, background): “This probability can be rewritten in terms of product of conditional probabilities by recursively applying the chain-rule (Manning et al., 1999; Bengio et al., 2003) as:”
Language Modeling with Gated Convolutional Networks (2016, background): “Analyzing the input hierarchically bears resemblance to classical grammar formalisms which build syntactic tree structures of increasing granuality, e.g., sentences consist of noun phrases and verb phrases each comprising further internal structure (Manning & Sch ¨ utze, 1999; Steedman, 2002).”
Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars (2005, background): “Miller et al. (1996) describe an approach that assumes full annotation of parse trees.”
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks (2016, methodology): “2 The AUROC sidesteps the issue of threshold selection, as does the Area Under the Precision-Recall curve (AUPR) which is sometimes deemed more informative (Manning & Sch¨utze, 1999).”
From Frequency to Meaning: Vector Space Models of Semantics (2010, background): “…words according to their parts of speech), word sense tagging (marking ambiguous words according to their intended meanings), and parsing (analyzing the grammatical structure of sentences and marking the words in the sentences according to their grammatical roles) (Manning & Schütze, 1999).”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “In addition, there exist several natural language processing tools such as part of speech taggers, word category classifiers, word embedding models, named entity recognition models, coreference resolution models, semantic role labeling models, semantic similarity models and sentiment analysis models”
Evaluating the State-of-the-Art of End-to-End Natural Language Generation: The E2E NLG Challenge (2019, methodology): “We further complement Shannon text entropy with n-gram-language-model-style conditional entropy for next-word prediction (Manning and Schütze 2000, p. 63ff.), given one previous word (bigram) or two previous words (trigram): Hcond(text) = − ∑ (c,w)∈ text freq(c, w) len(text) log2 ( freq(c,…”
Early Methods for Detecting Adversarial Images (2016, methodology): “The AUROC sidesteps the issue of selecting a threshold, as does the Area Under the Precision-Recall curve (AUPR) which is sometimes deemed more informative (Manning & Sch¨utze, 1999).”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “Moreover, we perform stemming [13], which maps inﬂected words onto a base form; e. g. “increased” and “increasing” are both mapped onto “increas” [39].”
Textual Sentiment in Finance: A Survey of Methods and Models (2014, neutral): “Documents are considered to be the bag of words, and the structure along with any linear ordering of words within the context is ignored (Manning and Schutze 1999).”
Joint Video and Text Parsing for Understanding Events and Answering Queries (2013, background): “An AOG is an extension of a constituency grammar used in natural language parsing [5] to represent hierarchical compositions of objects, scenes and events.”
Head-Driven Statistical Models for Natural Language Parsing (2003, methodology): “In the case of PCFGs, this can be accomplished using a variant of the CKY algorithm applied to weighted grammars (providing that the PCFG can be converted to an equivalent PCFG in Chomsky normal form); see, for example, Manning and Sch¨utze (1999).”
Multimodal Distributional Semantics (2013, methodology): “General overviews of semantic space models are provided by Clark (2013), Erk (2012), Manning and Schütze (1999), Sahlgren (2006) and Turney and Pantel (2010).”
Recognising Textual Entailment with Logical Inference (2005, methodology): “Here the predicate symbols from the DRS are mapped to WordNet synsets using a variant of Lesk’s WSD algorithm (Manning and Schuetze, 1999).”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, methodology): “In this paper, the term phrase-structure is used inter-changeably with sentence parse (Chomsky, 1957; Manning & Sch¨utze, 2003).”