language information retrieval applications
play

Language Information Retrieval applications: integration of - PowerPoint PPT Presentation

Jan 31, 2024 •280 likes •312 views

technology from seed Multi-word processing in domain specific Cross- Language Information Retrieval applications: integration of ontology-based methods and Linguistic resources Johanna Monti UNISS Maria Pia di Buono UNISA Mario

  1. technology from seed Multi-word processing in domain specific Cross- Language Information Retrieval applications: integration of ontology-based methods and Linguistic resources Johanna Monti – UNISS Maria Pia di Buono – UNISA Mario Monteleone – UNISA PARSEME - Frankfurt Workshop on MWEs, 8-9 September 2014, Frankfurt, Germany

  2. • Methodological approach to Cross-Language Information Retrieval (CLIR) applications for the development of a system which improves multi-word processing for specific domain translations. • Main features of the approach: • Multilingual ontology (International Council of Museums - Conseil International des Musees (ICOM – CIDOC) Conceptual Reference Model (CRM). CIDOC CRM) • Linguistic resources and tools (monolingual and bilingual MRDs, FSA/FSTs) • Data and metadata mapping among language-specific ontologies in the Cultural Heritage (CH) domain. PARSEME - Frankfurt Workshop on MWEs, 8-9 September 2014, Frankfurt, Germany

  3. System Workflow Linguistic Analysis → Source Language Electronic Dictionaries, FSA Transformation Routines → Semantic Annotation Translation Routines → Bilingual Electronic Dictionaries, FSTs PARSEME - Frankfurt Workshop on MWEs, 8-9 September 2014, Frankfurt, Germany

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

CS54701: Information Retrieval CS-54701 Information Retrieval Retrieval Models: Language models

CS54701: Information Retrieval CS-54701 Information Retrieval Retrieval Models: Language models

CS54701: Information Retrieval CS-54701 Information Retrieval Retrieval Models: Language models Luo Si Department of Computer Science Purdue University Retrieval Model: Language Model Introduction to language model Unigram language

934 views • 51 slides
Cross-Language Information Retrieval Carol Peters ISTI-CNR, Pisa Cross-Language Information

Cross-Language Information Retrieval Carol Peters ISTI-CNR, Pisa Cross-Language Information

Cross-Language Information Retrieval Carol Peters ISTI-CNR, Pisa Cross-Language Information Retrieval (CLIR) State-of-the-Art The basic cross-language text retrieval system technology now exists for bilingual and multilingual retrieval

117 views • 9 slides
Lecture 5: Language Modelling in Information Retrieval and Classification Information Retrieval

Lecture 5: Language Modelling in Information Retrieval and Classification Information Retrieval

Lecture 5: Language Modelling in Information Retrieval and Classification Information Retrieval Computer Science Tripos Part II Helen Yannakoudakis 1 Natural Language and Information Processing (NLIP) Group helen.yannakoudakis@cl.cam.ac.uk

961 views • 68 slides
Model Divergence Retrieval LM, session 10 CS6200: Information Retrieval Slides by: Jesse

Model Divergence Retrieval LM, session 10 CS6200: Information Retrieval Slides by: Jesse

Model Divergence Retrieval LM, session 10 CS6200: Information Retrieval Slides by: Jesse Anderton Retrieval With Language Models There are three obvious ways to perform retrieval using language models: 1. Query Likelihood Retrieval trains a

486 views • 9 slides
Analysis of Cross Language Information Retrieval methods Introduction to Cross Language

Analysis of Cross Language Information Retrieval methods Introduction to Cross Language

Analysis of Cross Language Information Retrieval methods Introduction to Cross Language Information Retrieval (CLIR) CLIR is a subfield of information retrieval dealing with retrieving information written in a language different from the

461 views • 18 slides
NPFL103: Information Retrieval (8) Language Models for Information Retrieval, Text Classification

NPFL103: Information Retrieval (8) Language Models for Information Retrieval, Text Classification

Language models Text classification Naive Bayes Evaluation of text classification NPFL103: Information Retrieval (8) Language Models for Information Retrieval, Text Classification Pavel Pecina Institute of Formal and Applied Linguistics

1.31k views • 59 slides
Information Retrieval Introducing Information Retrieval and Web Search Information Retrieval

Information Retrieval Introducing Information Retrieval and Web Search Information Retrieval

Introduction to Information Retrieval Introducing Information Retrieval and Web Search Information Retrieval Information Retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an

1.01k views • 57 slides
Information Retrieval Language

Information Retrieval Language

Information Retrieval Language Technology I Information Retrieval Traditional information retrieval is

439 views • 25 slides
Information Retrieval Natural Language Processing and Machine Leanring Advanced Natural Language

Information Retrieval Natural Language Processing and Machine Leanring Advanced Natural Language

Information Retrieval Natural Language Processing and Machine Leanring Advanced Natural Language Processing and Information Retrieval Summary of IR, NLP, Machine Learning Alessandro Moschitti Department of Computer Science and Information

1.82k views • 171 slides
Introduction to Information Retrieval http://informationretrieval.org IIR 1: Boolean Retrieval

Introduction to Information Retrieval http://informationretrieval.org IIR 1: Boolean Retrieval

Introduction to Information Retrieval http://informationretrieval.org IIR 1: Boolean Retrieval Hinrich Sch utze Center for Information and Language Processing, University of Munich 2014-04-09 1 / 60 Boolean retrieval The Boolean model is

1.34k views • 111 slides
Information Retrieval Ling573 NLP Systems & Applications April 15, 2014 Roadmap

Information Retrieval Ling573 NLP Systems & Applications April 15, 2014 Roadmap

Information Retrieval Ling573 NLP Systems & Applications April 15, 2014 Roadmap Information Retrieval Vector Space Model Term Selection & Weighting Evaluation Refinements: Query Expansion

514 views • 23 slides
Applications (1 of 2): Information Retrieval Kenneth Church Kenneth.Church@jhu.edu

Applications (1 of 2): Information Retrieval Kenneth Church Kenneth.Church@jhu.edu

Applications (1 of 2): Information Retrieval Kenneth Church Kenneth.Church@jhu.edu Kenneth.Church@jhu.edu Dec 2, 2009 1 Pattern Recognition Problems in Computational Linguistics Information Retrieval: Is this doc more like relevant docs

1.35k views • 87 slides
Introduction to Information Retrieval http://informationretrieval.org IIR 1: Boolean Retrieval

Introduction to Information Retrieval http://informationretrieval.org IIR 1: Boolean Retrieval

Boolean model and Inverted index Processing Boolean queries Why ranked retrieval? Introduction to Information Retrieval http://informationretrieval.org IIR 1: Boolean Retrieval Hinrich Sch utze Institute for Natural Language Processing,

1.59k views • 127 slides
CSE 7/5337: Information Retrieval and Web Search Dictionaries and tolerant retrieval (IIR 3)

CSE 7/5337: Information Retrieval and Web Search Dictionaries and tolerant retrieval (IIR 3)

CSE 7/5337: Information Retrieval and Web Search Dictionaries and tolerant retrieval (IIR 3) Michael Hahsler Southern Methodist University These slides are largely based on the slides by Hinrich Sch utze Institute for Natural Language

1.52k views • 108 slides
CSE 7/5337: Information Retrieval and Web Search Introduction and Boolean Retrieval (IIR 1)

CSE 7/5337: Information Retrieval and Web Search Introduction and Boolean Retrieval (IIR 1)

CSE 7/5337: Information Retrieval and Web Search Introduction and Boolean Retrieval (IIR 1) Michael Hahsler Southern Methodist University These slides are largely based on the slides by Hinrich Sch utze Institute for Natural Language

456 views • 35 slides
Query Likelihood Retrieval LM, session 6 CS6200: Information Retrieval Slides by: Jesse Anderton

Query Likelihood Retrieval LM, session 6 CS6200: Information Retrieval Slides by: Jesse Anderton

Query Likelihood Retrieval LM, session 6 CS6200: Information Retrieval Slides by: Jesse Anderton Retrieval With Language Models So far, weve focused on language models like P ( D = w 1 , w 2 , , w n ). Wheres the query? Remember the

446 views • 6 slides
The Lurch Project A word processor that checks your math Past, Present, and Future Nathan Carter

The Lurch Project A word processor that checks your math Past, Present, and Future Nathan Carter

The Lurch Project A word processor that checks your math Past, Present, and Future Nathan Carter Bentley University Category: Software for Experiencing Rules Not computational Maple does math for you. Lurch checks your work. Not a

482 views • 23 slides
Schemes for Pattern-Avoiding Words Lara Pudwell Rutgers University Permutation Patterns 2007

Schemes for Pattern-Avoiding Words Lara Pudwell Rutgers University Permutation Patterns 2007

Introduction/History Prefix Schemes for Words Other Schemes for Words Summary Schemes for Pattern-Avoiding Words Lara Pudwell Rutgers University Permutation Patterns 2007 Lara Pudwell Schemes for Pattern-Avoiding Words Introduction/History

1.06k views • 87 slides
Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word

Word Embeddings CS 6956: Deep Learning for NLP Overview Representing meaning Word embeddings: Early work Word embeddings via language models Word2vec and Glove Evaluating embeddings Design choices and open questions 1

1.25k views • 75 slides
iSCSI PDU Header Notes Octet 0 Octet 1 Octet 2 Octet 3 AHS Data Length Length Always 48

iSCSI PDU Header Notes Octet 0 Octet 1 Octet 2 Octet 3 AHS Data Length Length Always 48

iSCSI PDU Header Notes Octet 0 Octet 1 Octet 2 Octet 3 AHS Data Length Length Always 48 bytes Opcode Opcode-specific fields 1. AHS_length is given in 4 byte words allowing a maximum size of 1020 bytes for BHS all combined AHSes. 2.

327 views • 3 slides
Last Class COGS 105 Research Methods for Cognitive Scientists In any behavioral research we

Last Class COGS 105 Research Methods for Cognitive Scientists In any behavioral research we

Last Class COGS 105 Research Methods for Cognitive Scientists In any behavioral research we need to design measures, develop tasks, and recruit people to participate in them. Lots of sampling methods; usually we are stuck with

348 views • 9 slides
CENG3420 Lecture 08: Cache Bei Yu (Latest update: March 19, 2020) Spring 2020 1 / 40 Overview

CENG3420 Lecture 08: Cache Bei Yu (Latest update: March 19, 2020) Spring 2020 1 / 40 Overview

CENG3420 Lecture 08: Cache Bei Yu (Latest update: March 19, 2020) Spring 2020 1 / 40 Overview Introduction Direct Mapping Associative Mapping Replacement Conclusion 2 / 40 Overview Introduction Direct Mapping Associative Mapping

529 views • 50 slides
International Web Sites Richard Ishida 1 Version: 16 June 2009 International Web Sites Richard

International Web Sites Richard Ishida 1 Version: 16 June 2009 International Web Sites Richard

International Web Sites Richard Ishida 1 Version: 16 June 2009 International Web Sites Richard Ishida 2 Version: 16 June 2009 International Web Sites Richard Ishida 3 Version: 16 June 2009 International Web Sites Richard Ishida 4

1.19k views • 75 slides
Discourse Structure & Wrap-up: Q-A Ling571 Deep Processing Techniques for NLP March 8, 2017

Discourse Structure & Wrap-up: Q-A Ling571 Deep Processing Techniques for NLP March 8, 2017

Discourse Structure & Wrap-up: Q-A Ling571 Deep Processing Techniques for NLP March 8, 2017 Roadmap Discourse cohesion: Topic segmentation evaluation Discourse coherence: Shallow and deep discourse parsing

958 views • 46 slides

More recommend