Dan Jurafsky

Stanford University
0000-0002-6459-7745
http://web.stanford.edu/people/jurafsky
Publications337
h-index93
Citations43,266
Highly Influential Citations4,225
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Distant supervision for relation extraction without labeled data
TLDR
This work investigates an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size.
Speech and Language Processing
is one of the most recognizablecharacters in 20th century cinema. HAL is an artificial agent capable of such advancedlanguage behavior as speaking and understanding English, and at a crucial moment…
Speech and language processing - an introduction to natural language processing, computational linguistics, and speech recognition
TLDR
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora, to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation.
Deep Reinforcement Learning for Dialogue Generation
TLDR
This work simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity, non-repetitive turns, coherence, and ease of answering.
Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks
TLDR
This work explores the use of Amazon's Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web, and proposes a technique for bias correction that significantly improves annotation quality on two tasks.
Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change
TLDR
A robust methodology for quantifying semantic change is developed by evaluating word embeddings against known historical changes and it is revealed that words that are more polysemous have higher rates of semantic change.
Dialogue act modeling for automatic tagging and recognition of conversational speech
TLDR
A probabilistic integration of speech recognition with dialogue modeling is developed, to improve both speech recognition and dialogue act classification accuracy.
Adversarial Learning for Neural Dialogue Generation
TLDR
This work applies adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances, and investigates models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls.
Predictability Effects on Durations of Content and Function Words in Conversational English
In a regression study of conversational speech, we show that frequency, contextual predictability, and repetition have separate contributions to word duration, despite their substantial correlations.…
Automatic Labeling of Semantic Roles
TLDR
This work presents a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame, derived from parse trees and hand-annotated training data.
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