'Named entity recognition' presentation slideshows

Named Entity Recognition gate.ac.uk/ nlp.shef.ac.uk/ Hamish Cunningham

Named Entity Recognition http://gate.ac.uk/ http://nlp.shef.ac.uk/ Hamish Cunningham Kalina Bontcheva RANLP, Borovets, Bulgaria, 8 th September 2003. Structure of the Tutorial. task definition applications corpora, annotation evaluation and testing how to preprocessing

1.32k views • 110 slides

Named Entity Recognition gate.ac.uk/ nlp.shef.ac.uk/ Hamish Cunningham

Named Entity Recognition http://gate.ac.uk/ http://nlp.shef.ac.uk/ Hamish Cunningham Kalina Bontcheva RANLP, Borovets, Bulgaria, 8 th September 2003. Structure of the Tutorial. task definition applications corpora, annotation evaluation and testing how to preprocessing

1.24k views • 108 slides

Systems & Applications: Introduction

Systems & Applications: Introduction. Ling 573 NLP Systems and Applications March 29, 2011. Roadmap. Motivation 573 Structure Question-Answering Shared Tasks. Motivation. Information retrieval is very powerful Search engines index and search enormous doc sets

1.04k views • 97 slides

Text Mining: Techniques, Tools, ontologies and Shared tasks

Text Mining: Techniques, Tools, ontologies and Shared tasks. Xiao Liu, with updates from Shuo Yu Spring 2019. Introduction. Text mining, also referred to as text data mining, refers to the process of deriving high quality information from text.

1.04k views • 89 slides

899 views • 58 slides

An Introduction to Machine Learning and Natural Language Processing Tools

An Introduction to Machine Learning and Natural Language Processing Tools. Vivek Srikumar, Mark Sammons (Some slides from Nick Rizzolo). The Famous People Classifier. f( ) = Politician. f( ) = Athlete. f( ) = Corporate Mogul. Outline. An Overview of NLP Resources

1.03k views • 54 slides

(Pseudo)-Relevance Feedback & Passage Retrieval

(Pseudo)-Relevance Feedback & Passage Retrieval. Ling573 NLP Systems & Applications April 28, 2011. Roadmap. Retrieval system walkthrough Improving document retrieval Compression & Expansion techniques Passage retrieval: Contrasting techniques Interactions with document retreival.

755 views • 53 slides

CS 388: Natural Language Processing: Information Extraction

CS 388: Natural Language Processing: Information Extraction. Raymond J. Mooney University of Texas at Austin. 1. 1. Information Extraction (IE). Identify specific pieces of information (data) in a unstructured or semi-structured textual document.

729 views • 52 slides

A Maximum Entropy-based Model for Answer Extraction

A Maximum Entropy-based Model for Answer Extraction. Dan Shen IGK, Saarland University Supervisors: Prof. Dietrich Klakow Dr. ir. Geert-Jan M. Kruijff. Part I -- Introduction. Answer Extraction Module in QA Statistical Method for Answer Extraction Motivation Framework.

570 views • 38 slides

NLP Tools

NLP Tools. By : Asef pourmasoumi Hossein Kamyar. Supervisor : Dr. Kahani. Sentence splitter & Tokenizer Stemming Discourse analysis Coreference Resolution Named entity recognition (NER) Natural language generation Natural language understanding Part of speech tagging (POS)

493 views • 30 slides

Named Entity Recognition for Digitised Historical Texts

Named Entity Recognition for Digitised Historical Texts. by Claire Grover, Sharon Givon , Richard Tobin and Julian Ball (UK) presented by Thomas Packer. A Need for Named Entity Recognition. Goal and High-Level Process. Automatically index historical texts so they are searchable.

359 views • 23 slides

Overview of Statistical NLP

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Silver Medallion CRI Poster Review

321 views • 22 slides

Applications of Sequence Learning CMPT 825 Mashaal A. Memon

Applications of Sequence Learning CMPT 825 Mashaal A. Memon. What We Know of Sequence Learning. Part Of Speech (POS) Tagging is a sequence learning problem. 3 approaches to solving the problem:. Noisy-Channel Classification Rule-Based. What We Know About POS Tagging.

277 views • 20 slides

Chunking: Shallow Parsing

383 views • 18 slides

Normalizing Microtext

Normalizing Microtext. Zhenzhen Xue , Dawei Yin and Brian D. Davison Lehigh University. Introduction. Microtext Informal written text Limited length E.g., Tweets, texting messages, online chat messages High proportion of Non-Standard Words (NSWs)

318 views • 17 slides

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279 views • 14 slides

MetaMap /MTI Web API

MetaMap /MTI Web API. MetaMap/MTI Web API. MetaMap identifies biomedical concepts in text Medical Text Indexer (MTI) summarizes text using MetaMap and the Medical Subject Headings (MeSH) vocabulary. MetaMap/MTI Web API.

267 views • 9 slides

NLP Highlights

NLP Highlights. GS Savova And team. Sign/Symptom CEM template. Disease/Disorder CEM template. Medication CEM template. Alleviating_factor associatedCode Body_laterality Body_location Body_side Conditional Course Duration End_time Exacerbating_factor Generic Negation_indicator

181 views • 5 slides