'Training corpus' diaporamas de présentation

COMP 791A: Statistical Language Processing

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LIN3022 Natural Language Processing

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Information Extraction from Web Documents

Information Extraction from Web Documents. CS 652 Information Extraction and Integration Li Xu Yihong Ding. IR and IE . IR (Information Retrieval) Retrieves relevant documents from collections Information theory, probabilistic theory, and statistics IE (Information Extraction)

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Recognizing Metadata: Segmentation and Disfluencies

Recognizing Metadata: Segmentation and Disfluencies. Julia Hirschberg CS 4706. DARPA EARS Program. Effective, Affordable, Reusable Speech-to-Text Goals: Produce transcriptions that are more readable and usable by humans and downstream processes Segment speech into text-like units

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Detecting Misunderstandings in the CMU Communicator Spoken Dialog System

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Improving Bug Localization using Correlations in Crash Reports

Improving Bug Localization using Correlations in Crash Reports . David ( Shaohua ) Wang , Foutse Khomh , Ying Zou. Mozilla Firefox receives 2.5 million crash reports every day!. Crash reports analysis for localizing bugs is a challenging task. Related by bug id 514819 .

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Natural Language Processing : Probabilistic Context Free Grammars

Natural Language Processing : Probabilistic Context Free Grammars. Updated 8/07. Motivation. N-gram models and HMM Tagging only allowed us to process sentences linearly.

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Helmut Schmid: Probabilistic Part-of-Speech-Tagging Using Decision Trees

Helmut Schmid: Probabilistic Part-of-Speech-Tagging Using Decision Trees. Editing: Nicolai Kilian 30.11.2004. Overview. What are Part-Of-Speech-taggers? What different POS-taggers are there? Common properties between n-gram-taggers and tree-taggers

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Natural Language Processing : Probabilistic Context Free Grammars

Natural Language Processing : Probabilistic Context Free Grammars. Updated 29/12/2005. Motivation. N-gram models and HMM Tagging only allowed us to process sentences linearly.

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Lecture 6: The Ultimate Authorship Problem: Verification for Short Docs

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Part-Of-Speech Tagging using Neural Networks

Part-Of-Speech Tagging using Neural Networks. Ankur Parikh LTRC IIIT Hyderabad ankur.parikh85@gmail.com. Outline. 1.Introduction 2.Background and Motivation 3.Experimental Setup 4.Preprocessing 5.Representation 6.Single-neuro tagger 7.Experiments 8.Multi-neuro tagger 9.Results

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Noun Homograph Disambiguation Using Local Context in Large Text Corpora

Noun Homograph Disambiguation Using Local Context in Large Text Corpora. Marti A. Hearst Presented by: Heng Ji Mar. 29, 2004. Outline. Introduction Motivations of Algorithm Feature Selection Crucial Problem and Detail Algorithm Experiment Results Conclusions & Discussions.

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Phrase Reordering for Statistical Machine Translation Based on Predicate-Argument Structure

Phrase Reordering for Statistical Machine Translation Based on Predicate-Argument Structure. Mamoru Komachi, Yuji Matsumoto Nara Institute of Science and Technology Masaaki Nagata NTT Communication Science Laboratories. ??. ?. ??. ?. ??. ?. ???. write. please. address. -ACC. -LOC.

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In Search of a More Probable Parse: Experiments with DOP* and the Penn Chinese Treebank

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Shallow Parsing

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The LC-STAR project ( IST-2001-32216 ) Objectives: Track I (duration 2 years)

Creation of Lexica for Speech Centered Translation Ute Ziegenhain (Siemens AG), Asuncion Moreno (UPC), Nuria Castell (UPC). www.lcstar.com. The LC-STAR project ( IST-2001-32216 ) Objectives: Track I (duration 2 years)

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Learning Task

2. 3. 4. 5. 1. System interacts with each simulation model 40,000 times to generate training corpora. Train MDP policies from each training corpus. Implement the policies into 3 new systems. New systems interact with PM 500 times each to generate testing corpora. Apply Evaluation Measures.

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