His main research concerns Artificial intelligence, Natural language processing, Parsing, Word and Inference. The Artificial intelligence study combines topics in areas such as Machine learning, Task and Set. His work carried out in the field of Natural language processing brings together such families of science as Annotation and FrameNet.
His studies in Parsing integrate themes in fields like Dependency, Representation and Graph. His Word study combines topics in areas such as WordNet, Space, Binary number and Embedding. His biological study spans a wide range of topics, including Commonsense reasoning and Translation.
Noah A. Smith mostly deals with Artificial intelligence, Natural language processing, Machine learning, Parsing and Task. His Word, Language model, Dependency grammar, Inference and Natural language investigations are all subjects of Artificial intelligence research. Noah A. Smith regularly ties together related areas like Variety in his Language model studies.
As part of his studies on Natural language processing, he frequently links adjacent subjects like Context. His specific area of interest is Machine learning, where he studies Structured prediction. Noah A. Smith has included themes like Dependency, Probabilistic logic and Theoretical computer science in his Parsing study.
Artificial intelligence, Natural language processing, Language model, Machine learning and Task are his primary areas of study. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Code. His research in Natural language processing intersects with topics in Word and Vocabulary.
His Word research incorporates themes from Range and Coreference. The study incorporates disciplines such as Domain, Semantics, Transformer and Machine translation in addition to Language model. His studies in Machine learning integrate themes in fields like Test data, Variety and State.
Noah A. Smith focuses on Artificial intelligence, Machine learning, Language model, Natural language processing and Word. Noah A. Smith has researched Artificial intelligence in several fields, including Test data, Task and Set. His studies deal with areas such as Data mapping, Benchmark, State and Code as well as Machine learning.
His studies examine the connections between Language model and genetics, as well as such issues in Transformer, with regards to Recurrent neural network and Computer engineering. His Natural language processing study incorporates themes from Similarity and Coreference. His work carried out in the field of Word brings together such families of science as Polyglot, Semantic similarity, Range, Dependency grammar and Reinforcement learning.
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From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
Brendan O'Connor;Ramnath Balasubramanyan;Bryan R. Routledge;Noah A. Smith.
international conference on weblogs and social media (2010)
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
Kevin Gimpel;Nathan Schneider;Brendan O'Connor;Dipanjan Das.
meeting of the association for computational linguistics (2011)
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
Olutobi Owoputi;Brendan O'Connor;Chris Dyer;Kevin Gimpel.
north american chapter of the association for computational linguistics (2013)
The Web as a parallel corpus
Philip Resnik;Noah A. Smith.
Computational Linguistics (2003)
A Latent Variable Model for Geographic Lexical Variation
Jacob Eisenstein;Brendan O'Connor;Noah A. Smith;Eric P. Xing.
empirical methods in natural language processing (2010)
A Simple, Fast, and Effective Reparameterization of IBM Model 2
Chris Dyer;Victor Chahuneau;Noah A. Smith.
north american chapter of the association for computational linguistics (2013)
Transition-Based Dependency Parsing with Stack Long Short-Term Memory
Chris Dyer;Miguel Ballesteros;Wang Ling;Austin Matthews.
arXiv: Computation and Language (2015)
Retrofitting Word Vectors to Semantic Lexicons
Manaal Faruqui;Jesse Dodge;Sujay Kumar Jauhar;Chris Dyer.
north american chapter of the association for computational linguistics (2015)
Better Hypothesis Testing for Statistical Machine Translation: Controlling for Optimizer Instability
Jonathan H. Clark;Chris Dyer;Alon Lavie;Noah A. Smith.
meeting of the association for computational linguistics (2011)
What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
Mengqiu Wang;Noah A. Smith;Teruko Mitamura.
empirical methods in natural language processing (2007)
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