'Word sense disambiguation' presentation slideshows

Word sense disambiguation - PowerPoint PPT Presentation


Advanced best match

Advanced best match

Advanced best match Mark Sanderson Porto, 2000 Aims To build on what you have done so far, reviewing more sophisticated ways of ranking documents in relation to a query. Objectives At the end of this lecture you will be able to describe a range of statistically based approaches to IR namely

By lotus
(332 views)

Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation

Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation

Combining Lexical and Syntactic Features for Supervised Word Sense Disambiguation. Masters Thesis : Saif Mohammad Advisor : Dr. Ted Pedersen University of Minnesota, Duluth. Path Map. Introduction Background Data Experiments Conclusions. Word Sense Disambiguation.

By Jimmy
(295 views)

Learners’ Dictionary

Learners’ Dictionary

Learners’ Dictionary. Deny A. Kwary www.kwary.net. Today’s Topics. The Objective of Designing MLDs The History of MLDs The Main features of MLDs The Impact of Corpora The Sketch Engine. 1. The Objective of Designing MLDs.

By eloise
(258 views)

I256 Applied Natural Language Processing Fall 2009

I256 Applied Natural Language Processing Fall 2009

I256 Applied Natural Language Processing Fall 2009. Lecture 5 Word Sense Disambiguation (WSD) Intro on Probability Theory Graphical Models Naïve Bayes Naïve Bayes for WSD . Barbara Rosario. Word Senses. Words have multiple distinct meanings, or senses:

By phyllis
(160 views)

CMSC 723 / LING 645: Intro to Computational Linguistics

CMSC 723 / LING 645: Intro to Computational Linguistics

CMSC 723 / LING 645: Intro to Computational Linguistics. September 1, 2004: Dorr Overview, History, Goals, Problems, Techniques; Intro to MT (J&M 1, 21) Prof. Bonnie J. Dorr Dr. Christof Monz TA: Adam Lee. Administrivia. http://www.umiacs.umd.edu/~christof/courses/cmsc723-fall04/. IMPORTANT:

By morna
(119 views)

Extended Gloss Overlaps as a Measure of Semantic Relatedness

Extended Gloss Overlaps as a Measure of Semantic Relatedness

Extended Gloss Overlaps as a Measure of Semantic Relatedness. Satanjeev Banerjee Ted Pedersen Carnegie Mellon University University of Minnesota Duluth Supported by NSF Grants: #0092784, REC-9979894. Semantic Relatedness.

By Mia_John
(156 views)

Outline

Outline

Outline. Linguistic Theories of semantic representation Case Frames – Fillmore – FrameNet Lexical Conceptual Structure – Jackendoff – LCS Proto-Roles – Dowty – PropBank English verb classes (diathesis alternations) - Levin - VerbNet Manual Semantic Annotation

By beau
(174 views)

Effective and Efficient Historical Memory Retrieval Bias in Soar’s Semantic Memory

Effective and Efficient Historical Memory Retrieval Bias in Soar’s Semantic Memory

Effective and Efficient Historical Memory Retrieval Bias in Soar’s Semantic Memory. Nate Derbinsky University of Michigan. Semantic Memory in Soar. Motivation Some knowledge can be useful independent of the context in which it was initially learned

By Mia_John
(144 views)

Comparing Ontology-based and Corpus-based Domain Annotations in WordNet.

Comparing Ontology-based and Corpus-based Domain Annotations in WordNet.

Comparing Ontology-based and Corpus-based Domain Annotations in WordNet. A paper by: Bernardo Magnini Carlo Strapparava Giovanni Pezzulo Alfio Glozzo Presented by : rabee ali alshemali. Motive. Domain information is an emerging topic of interest in relation to WrodNet . Proposal

By keene
(176 views)

Adam Kilgarriff doesn’t believe in word senses….

Adam Kilgarriff doesn’t believe in word senses….

Adam Kilgarriff doesn’t believe in word senses…. *. (“I don’t believe in Word Senses”, A. Kilgarriff, in Computers and the Humanities 31 (2) pp 91-113, 1997) A summary by Peter Clark Boeing Research. * and Sue Atkins, the source of the original quote. Word Sense Disambiguation.

By neo
(164 views)

Improving Machine Learning Approaches to Coreference Resolution Vincent Ng and Claire Cardie Department of Computer Scie

Improving Machine Learning Approaches to Coreference Resolution Vincent Ng and Claire Cardie Department of Computer Scie

Improving Machine Learning Approaches to Coreference Resolution Vincent Ng and Claire Cardie Department of Computer Science Cornell University. ACL 2002, Univ. of Pennsylvania, Philadelphia, PA (July 2002) Session: Anaphora and Coreference Session Chair: Lillian Lee.

By tim
(1 views)

Word Sense Disambiguation

Word Sense Disambiguation

Word Sense Disambiguation. Ling571 Deep Processing Techniques for NLP February 23, 2011. Word Sense Disambiguation. Robust Approaches Supervised Learning Approaches Naïve Bayes Dictionary-based Approaches Bootstrapping Approaches One sense per discourse/ collocation

By gaston
(352 views)

Crowdsourcing for NLP Using Amazon Mechanical Turk and CrowdFlower Matteo Negri and Yashar Mehdad

Crowdsourcing for NLP Using Amazon Mechanical Turk and CrowdFlower Matteo Negri and Yashar Mehdad

Crowdsourcing for NLP Using Amazon Mechanical Turk and CrowdFlower Matteo Negri and Yashar Mehdad. Crowdsourcing. Wikipedia :

By sonja
(108 views)

LING / C SC 439/539 Statistical Natural Language Processing

LING / C SC 439/539 Statistical Natural Language Processing

LING / C SC 439/539 Statistical Natural Language Processing. Lecture 20 4 /1/2013. Recommended reading. Word Sense Disambiguation Jurafsky & Martin 20.0-20.4

By kyle
(157 views)

Collective Word Sense Disambiguation

Collective Word Sense Disambiguation

Collective Word Sense Disambiguation. David Vickrey Ben Taskar Daphne Koller. Word Sense Disambiguation. Clues. The electricity plant supplies 500 homes with power . vs. A plant requires water and sunlight to survive. Clues. Tricky:. That plant produces bottled water.

By whitby
(100 views)

Annotation for Hindi PropBank

Annotation for Hindi PropBank

Annotation for Hindi PropBank. Outline. Introduction to the project Basic linguistic concepts Verb & Argument Making information explicit Null arguments. Tasks to be carried out Timesheets, tips. Creation of Resources. For machines rather than humans

By obert
(151 views)

Maxent Models and Discriminative Estimation

Maxent Models and Discriminative Estimation

Maxent Models and Discriminative Estimation. Generative vs. Discriminative models Christopher Manning. Introduction. So far we’ve looked at “generative models” Language models, Naive Bayes

By vivi
(121 views)

Presenter: Chun-Ping Wu

Presenter: Chun-Ping Wu

Unsupervised word sense disambiguation for Korean through the acyclic weighted digraph using corpus and dictionary. Presenter: Chun-Ping Wu Authors: Yeohoon Yoon, Choong-Nyoung Seon , Songwook Lee, Jungynu Seo. 國立雲林科技大學 National Yunlin University of Science and Technology. IPM 2007.

By river
(94 views)

NLM Indexing Initiative Tools for NLP: MetaMap and the Medical Text Indexer

NLM Indexing Initiative Tools for NLP: MetaMap and the Medical Text Indexer

NLM Indexing Initiative Tools for NLP: MetaMap and the Medical Text Indexer. Natural Language Processing: State of the Art, Future Directions April 23, 2012 Alan R. Aronson . Outline. Introduction MetaMap Overview Linguistic roots Recent Word Sense Disambiguation (WSD) efforts

By dusty
(135 views)

Word Sense Disambiguation

Word Sense Disambiguation

Word Sense Disambiguation. Asma Naseer. (Slides from Dr. Mary P. Harper, http://min.ecn.purdue.edu/~ee669/). Overview of the Problem. Problem: many words have different meanings or senses, i.e., there is ambiguity about how they are to be specifically interpreted (e.g., differentiate ).

By grazia
(106 views)

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