Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition

By Dan Jurafsky, James H. Martin. 2000

2,609 citations recorded by the source corpus.

Subfields: Language and Linguistics, Natural Language Processing

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Citations in the corpus

Ethical and social risks of harm from Language Models (2021, background): “In parallel, James Baker (Baker, 1990) developed a similar approach to ASR (see (Jurafsky and Martin, 2014)).”
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (2013, background): “…     2×log(f (s i )) log(f (s))+log(f (s i )) s i ∈ H(s) 1 s w = s i 0 otherwise (23) 3.5.3 Distributional Similarity (σ D C ) Distributional similarity metrics are based on the observation that words that are similar to each other tend to appear in similar contexts (Jurafsky & Martin, 2008).”
Does Gender Matter? Towards Fairness in Dialogue Systems (2019, background): “Task-oriented dialogue agents, such as Apple Siri and Microsoft Cortana, assist users to complete specific tasks such as trip planning and restaurant reservations (Jurafsky and Martin, 2009).”
Survey of Hallucination in Natural Language Generation (2022, background): “A task-oriented dialogue system is often composed of several modules: a natural language understanding (NLU) module, a dialogue manager (DM), and a natural language generation (NLG) module [53, 80].”
A Neural Conversational Model (2015, methodology): “We can use this model for machine translation, question/answering, and conversations without major changes in the architecture.”
A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version (2015, methodology): “…coreference resolution models, semantic role labeling models, semantic similarity models and sentiment analysis models (Manning and Schütze, 1999; Jurafsky and Martin, 2008; Mikolov et al., 2013; Gurevych and Strube, 2004; Lin and Walker, 2011b) that may be used by the Natural Language…”
Marginal Release Under Local Differential Privacy (2017, background): “many algorithms in statistical language modeling/predictive text [26] and association rule mining (market basket analysis) compute low order marginals as a preprocessing step.”
Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, methodology): “…concerns the second assumption, note that in many cases vendors need to hand out their classiﬁers to end-users embedding them within the software executable or apparatuses; as such, an adversary would be able to extract the classiﬁer using, for instance, techniques based on dynamic binary analysis.”
Gmail Smart Compose: Real-Time Assisted Writing (2019, background): “Language modelling is a fundamental and indispensable component of many natural language processing (NLP) and automatic speech recognition (ASR) systems [23, 27, 32, 35].”
Visual Question Generation as Dual Task of Visual Question Answering (2017, background): “Introduction Question answering (QA) and question generation (QG) are two fundamental tasks in natural language processing (Manning, Schütze, and others 1999; Martin and Jurafsky 2000).”
Asking for Help Using Inverse Semantics (2014, methodology): “Traditional methods for generating language rely on a dedicated language-generation system that is not integrated with a language-understanding framework [Jurafsky and Martin, 2008, Reiter and Dale, 2000].”
Deep learning code fragments for code clone detection (2016, background): “For example, since they are simply smoothed counts of term co-occurrences, they are limited in their ability to generalize beyond the explicit features observed in training [29,75– 77].”
Natural language processing: state of the art, current trends and challenges (2017, background): “[101] HMM is not restricted to this application it has several others such as bioinformatics problems, for example, multiple sequence alignment [102].”
Natural language to SQL: Where are we today? (2020, background): “Dependency parsing is a task for ﬁnding syntactic dependencies of a given sentence [23].”
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook (2018, neutral): “As such, there are many techniques and tasks in NLP (c.f., Liu, 2011; Jurafsky and Martin, 2008).”