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Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need. The information need can be specified in the form of a search query. In the case of document retrieval, queries can be based on full-text or other content-based indexing. Information retrieval is the science of searching for information in a document, searching for documents themselves, and also searching for the metadata that describes data, and for databases of texts, images or sounds.
Vladimir Naumovich Vapnik is a computer scientist, researcher, and academic. He is one of the main developers of the Vapnik–Chervonenkis theory of statistical learning and the co-inventor of the support-vector machine method and support-vector clustering algorithms.
Michael John Witbrock is a computer scientist in the field of artificial intelligence. Witbrock is a native of New Zealand and is the former vice president of Research at Cycorp, which is carrying out the Cyc project in an effort to produce a genuine artificial intelligence.
Bernhard Schölkopf is a German computer scientist known for his work in machine learning, especially on kernel methods and causality. He is a director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he heads the Department of Empirical Inference. He is also an affiliated professor at ETH Zürich, honorary professor at the University of Tübingen and Technische Universität Berlin, and chairman of the European Laboratory for Learning and Intelligent Systems (ELLIS).
Michael Irwin Jordan is an American scientist, professor at the University of California, Berkeley, research scientist at the Inria Paris, and researcher in machine learning, statistics, and artificial intelligence.
Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing is a statistical technique for the analysis of two-mode and co-occurrence data. In effect, one can derive a low-dimensional representation of the observed variables in terms of their affinity to certain hidden variables, just as in latent semantic analysis, from which PLSA evolved.
Frederick Jelinek was a Czech-American researcher in information theory, automatic speech recognition, and natural language processing. He is well known for his oft-quoted statement, "Every time I fire a linguist, the performance of the speech recognizer goes up".
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W. Bruce Croft is a distinguished professor of computer science at the University of Massachusetts Amherst whose work focuses on information retrieval. He is the founder of the Center for Intelligent Information Retrieval and served as the editor-in-chief of ACM Transactions on Information Systems from 1995 to 2002. He was also a member of the National Research Council Computer Science and Telecommunications Board from 2000 to 2003. Since 2015, he is the Dean of the College of Information and Computer Sciences at the University of Massachusetts Amherst. He was Chair of the UMass Amherst Computer Science Department from 2001 to 2007.
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In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover the hidden thematic structure of a collection of documents. The algorithm improves upon earlier topic models such as latent Dirichlet allocation (LDA) by modeling correlations between topics in addition to the word correlations which constitute topics. PAM provides more flexibility and greater expressive power than latent Dirichlet allocation. While first described and implemented in the context of natural language processing, the algorithm may have applications in other fields such as bioinformatics. The model is named for pachinko machines—a game popular in Japan, in which metal balls bounce down around a complex collection of pins until they land in various bins at the bottom.
In statistics and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently: "dog" and "bone" will appear more often in documents about dogs, "cat" and "meow" will appear in documents about cats, and "the" and "is" will appear approximately equally in both. A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words. The "topics" produced by topic modeling techniques are clusters of similar words. A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is.
Andrew McCallum is a professor in the computer science department at University of Massachusetts Amherst. His primary specialties are in machine learning, natural language processing, information extraction, information integration, and social network analysis.
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Wei Wang is a Chinese-born American computer scientist. She is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi). Her research specializes in big data analytics and modeling, database systems, natural language processing, bioinformatics and computational biology, and computational medicine.
Yejin Choi is Wissner-Slivka Chair of Computer Science at the University of Washington. Her research considers natural language processing and computer vision.
Edward Y. Chang is a computer scientist, academic, and author. He is an adjunct professor of Computer Science at Stanford University, and Visiting Chair Professor of Bioinformatics and Medical Engineering at Asia University, since 2019.
Smita Krishnaswamy is an American scientist and associate professor in genetics and computer science at Yale University. She specializes in the development of machine learning techniques to analyze high-dimensional high-throughput biomedical data with applications in immunology, immunotherapy, cancer, neuroscience, developmental biology and health outcomes. She organized the Open Problems in Single-Cell Biology effort with the Chan Zuckerberg Initiative and remains a scientific advisor for the project.
References
- 1 2 "John Lafferty (IEEE Fellow in 2007)". IEEE. 2007. Retrieved 15 December 2014.
- 1 2 3 4 "Test-of-Time Award ICML'11". ICML. 2011. Retrieved 15 December 2014.
- 1 2 3 4 "Test-of-Time Award ICML'12". ICML. 2012. Retrieved 15 December 2014.
- 1 2 3 4 "Two classic paper prizes for papers that appeared at ICML 2013". ICML. 2013. Retrieved 15 December 2014.
- 1 2 3 4 "SIGIR 2014 Best Paper Awards". SIGIR. 2014. Retrieved 15 December 2014.
- 1 2 "John Lafferty appointed the Malone Professor of Statistics and Data Science". Yale University. November 2, 2017. Retrieved October 10, 2021.
- 1 2 Peter F. Brown; John Cocke (June 1990). "A statistical approach to machine translation". Computational Linguistics. 16 (2). MIT Press: 79–85. Retrieved 14 December 2014.
- ↑ "John Lafferty bio (ICMLA'06)" (PDF). ICMLA. 2006. Retrieved 15 December 2014.
- ↑ "JMLR Editorial Board". JMLR. Retrieved 15 December 2014.
- ↑ "Member Biographies (CATS)". Committee on Applied and Theoretical Statistics (CATS). National Research Council. Retrieved 15 December 2014.
- ↑ Philipp Koehn (2009). Statistical Machine Translation. Cambridge University Press. p. 17. ISBN 978-0521874151 . Retrieved 22 March 2015.
In the late 1980s, the idea of statistical machine translation was born in the labs of IBM Research.
