'Unigram model' presentation slideshows

Latent Dirichlet Allocation (LDA)

Latent Dirichlet Allocation (LDA). Week 8. Motivation. What do we want to do with text corpora? classification, novelty detection, summarization and similarity/relevance judgments. Given a text corpora or other collection of discrete data we wish to: Find a short description of the data.

1k views • 55 slides

A Bayesian approach to word segmentation: Theoretical and experimental results

A Bayesian approach to word segmentation: Theoretical and experimental results. Sharon Goldwater Department of Linguistics Stanford University. Word segmentation. One of the first problems infants must solve when learning language. Infants make use of many different cues.

540 views • 42 slides

A Bayesian approach to word segmentation: Theoretical and experimental results

A Bayesian approach to word segmentation: Theoretical and experimental results. Sharon Goldwater Department of Linguistics Stanford University. Word segmentation. One of the first problems infants must solve when learning language. Infants make use of many different cues.

425 views • 42 slides

Latent Dirichlet Allocation

587 views • 32 slides

Distributional Cues to Word Boundaries: Context Is Important

Distributional Cues to Word Boundaries: Context Is Important. Sharon Goldwater Stanford University. Tom Griffiths UC Berkeley. Mark Johnson Microsoft Research/ Brown University. Word segmentation. One of the first problems infants must solve when learning language.

402 views • 27 slides

Learning a Monolingual Language Model from a Multilingual Text Database

Learning a Monolingual Language Model from a Multilingual Text Database. Rayid Ghani & Rosie Jones School of Computer Science Carnegie Mellon University. CIKM 2000. Sample Docs. Sample Docs. What is a LM?. Probability distribution over terms in the language

324 views • 24 slides

Chapter 23: Probabilistic Language Models

Chapter 23: Probabilistic Language Models. April 13, 2004. Corpus-Based Learning. Information Retrieval Information Extraction Machine Translation. 23.1 Probabilistic Language Models. There are several advantages Can be trained from data Robust (accept any sentence)

533 views • 20 slides

Language Modeling

281 views • 13 slides