Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition

By D. Jurafsky, James H. Martin. 2000

917 citations recorded by the source corpus.

Subfields: Natural Language Processing, Computational Linguistics & Semantics, Speech & Audio

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Citations in the corpus

Emergent Abilities of Large Language Models (2022, neutral): “For instance, NLP has historically focused on task-specific models (Jurafsky and Martin, 2009).”
Book Review: Spoken Dialogue Technology: Toward the Conversational User Interface, by Michael F. McTear (2002, neutral): “For example, the word “the” may be pronounced “ni” in a phrase such as “in the event” due to assimilation of the /th/ phoneme to the preceding nasal phoneme /n/ as well as assimilation of the /uh/ vowel to the following vowel in the word “event” (Jurafsky and Martin, 2000).”
Language Models for Machine Translation: Original vs. Translated Texts (2011, methodology): “Formally, the perplexity PP of a language model L on a test set W = w 1 w 2 . . . w N is the probability of W normalized by the number of words N (Jurafsky and Martin 2008, page 96): For the second hypothesis, we extend the experiment to LMs created from texts translated from other languages.”
Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding (2023, neutral): “This framework enables us to employ beam search (Jurafsky and Martin, 2009; Graves, 2012) decoding tailored for intermediate steps and guide the beam searching process by controlling the error of each reasoning step to prevent potential error accumulation throughout the chaining.”
Improved pos tagging for text-to-speech synthesis (2011, background): “This operation relies on a number of natural language sub-tasks, ranging from text normalization to semantic parsing [4].”
Sociology in the Era of Big Data: The Ascent of Forensic Social Science (2015, neutral): “One variety of method has arisen in the field of computational linguistics and is used to identify patterns in speech and texts (Jurafsky and Martin 2009; Manning and Schütze 1999).”
Discriminative Reranking for Semantic Parsing (2006, methodology): “S CISSOR is based on a fairly standard approach to compositional semantics (Jurafsky and Martin, 2000).”
Packet Chasing: Spying on Network Packets over a Cache Side-Channel (2019, neutral): “To estimate the error rate, we again use edit Levenshtein distance [71] between the sent and received data for the pseudo random bit sequence.”
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts (2013, methodology): “…[25] was initially used by [30] for word sense disambiguation; on the other hand, the idea of classifying entities based on their context signatures [21] is also similar with distributional methods in word sense disambiguation [31], in which contexts of mentions are used to determine word senses.”
On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation (2008, background): “WSD has received the attention of many groups of researchers, with general NLP books dedicating separate chapters to WSD (Manning & Sch¨utze, 1999; Jurafsky & Martin, 2000; Dale, Moisl, & Somers, 2000), special issues on WSD in NLP journals (Ide & Veronis, 1998; Edmonds & Kilgarriﬀ, 2002), and…”
Hierarchical Transfer Learning for Multilingual, Multi-Speaker, and Style Transfer DNN-Based TTS on Low-Resource Languages (2020, background): “nounce them by producing sound waves automatically [2].”
Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation (2014, background): “Coreference resolution is a key task in natural language processing (Jurafsky and Martin, 2008) aiming to detect the referential expressions ( mentions ) in a text that point to the same entity.”