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 lotusCombining 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 JimmyLearners’ 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 eloiseI256 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 phyllisCMSC 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 mornaExtended 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_JohnOutline. 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 beauEffective 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_JohnComparing 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 keeneAdam 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 neoImproving 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 timWord 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 gastonCrowdsourcing for NLP Using Amazon Mechanical Turk and CrowdFlower Matteo Negri and Yashar Mehdad. Crowdsourcing. Wikipedia :
By sonjaLING / 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 kyleCollective 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 whitbyAnnotation 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 obertMaxent Models and Discriminative Estimation. Generative vs. Discriminative models Christopher Manning. Introduction. So far we’ve looked at “generative models” Language models, Naive Bayes
By viviUnsupervised 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 riverNLM 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 dustyWord 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 ).
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