'Language models' presentation slideshows

Agreement matters: Challenges of translating into a MRL

Agreement matters: Challenges of translating into a MRL. Yoav Goldberg Ben Gurion University. Why should we care about syntactic modeling of MRLs?. A brief summary. A brief summary. What Kevin said. Example: English ? Hebrew. I wash the car . Example: English ? Hebrew.

1.19k views • 108 slides

Data-Intensive Computing with MapReduce

Data-Intensive Computing with MapReduce. Session 4: Language Models and Inverted Indexing. Jimmy Lin University of Maryland Thursday, February 14, 2013.

1.03k views • 82 slides

The Future of Search

982 views • 75 slides

914 views • 74 slides

Data-Intensive Distributed Computing

Data-Intensive Distributed Computing. CS 431/631 451/651 (Fall 2019). Part 3: Analyzing Text (1/2). September 26, 2019. Ali Abedi. These slides are available at https://www.student.cs.uwaterloo.ca/~cs451.

756 views • 60 slides

Automatic Speech Recognition (ASR): A Brief Overview

663 views • 45 slides

Language Models for Information Retrieval

Language Models for Information Retrieval. Berlin Chen Department of Computer Science & Information Engineering National Taiwan Normal University. References: 1. W. B. Croft and J. Lafferty (Editors). Language Modeling for Information Retrieval. July 2003

1.21k views • 42 slides

MODELLING LATVIAN LANGUAGE FOR AUTOMATIC SPEECH RECOGNITION

MODELLING LATVIAN LANGUAGE FOR AUTOMATIC SPEECH RECOGNITION. Askars Salimbajevs. Supervisor: Prof. Inguna Skadi?a University of Latvia. Introduction Acoustic modelling Automatic data acquisition Pronunciation model Language modelling Text preprocessing Word and sub-word N-gram models

688 views • 42 slides

Sparse Information Extraction: Unsupervised Language Models to the Rescue

Sparse Information Extraction: Unsupervised Language Models to the Rescue. Doug Downey, Stef Schoenmackers, Oren Etzioni Turing Center University of Washington. Answering Questions on the Web. Q: Who has won a best actor Oscar for playing a villain?

433 views • 36 slides

Detecting Promotional Content in Wikipedia

Detecting Promotional Content in Wikipedia. Shruti Bhosale Heath Vinicombe Ray Mooney University of Texas at Austin. Outline. Introduction Related Work Our contribution Evaluation Conclusion. Outline. Introduction Related Work Our contribution Evaluation Conclusion.

434 views • 34 slides

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Sp

CS 552/652 Speech Recognition with Hidden Markov Models Winter 2011 Oregon Health & Science University Center for Spoken Language Understanding John-Paul Hosom Lecture 14 February 23 Language Models. Language Models.

535 views • 32 slides

CMU-Statistical Language Modeling & SRILM Toolkits

557 views • 28 slides

412 views • 28 slides

Information Extraction from Spoken Language

386 views • 25 slides

Prosody and NLP

Seminar by Nikhil : 06005004 Adith : 06005005 Prachur : 06D05011. Prosody and NLP. We have a presentation this Friday ? We have a presentation this Friday ? We have a presentation this Friday ?. Abstract.

383 views • 24 slides

Moses, past, present, future

Moses, past, present, future. Hieu Hoang XRCE 2013. Timeline. What is Moses?. Common Misconceptions. Only the decoder Only for Linux Difficult to use Unreliable Only phrase- based No sparse features Developed by one person Slow. Only the decoder. replacement for Pharoah

304 views • 22 slides

Two-stage Language Models for Information Retrieval

Two-stage Language Models for Information Retrieval. ChengXiang Zhai*, John Lafferty School of Computer Science Carnegie Mellon University. *New Address Department of Computer Science University of Illinois, Urbana-Champaign. Motivation. Retrieval parameters are needed to

315 views • 21 slides

IITB @ FIRE 2010: Discriminative Models for IR

IITB @ FIRE 2010: Discriminative Models for IR. Vishal Vachhani, Shalini Gupta Joint work with Manoj Chinnakotla, Karthik Raman, Pushpak Bhattacharyya 20 th February, 2010. Roadmap. Introduction Ranking Models for IR Discriminative Models for IR Features for RankSVM Training & PRF

320 views • 20 slides

Overview of Statistical Language Models

Overview of Statistical Language Models. ChengXiang Zhai Department of Computer Science University of Illinois, Urbana-Champaign. Outline. What is a statistical language model (SLM)? Brief history of SLM Types of SLM Applications of SLM. What is a Statistical Language Model (LM)?.

682 views • 18 slides

Natural Language Models for Predicting Programming Comments

Natural Language Models for Predicting Programming Comments . Dana Movshovitz-Attias , William Cohen Aug 5, 2013 ACL 2013. Modeling Software Code. Code follows syntax rules, but is written by humans It is repetitive and predictable, similar to natural language [ Hindle et al. 2012].

298 views • 17 slides