'Weighted edit distance' presentation slideshows

Tolerant IR

1.29k views • 115 slides

An Introduction to IR

An Introduction to IR. Lecture 3 Dictionaries and Tolerant retrieval. Tokenizer. Friends. Romans. Countrymen. Token stream. Linguistic modules. friend. friend. roman. countryman. Modified tokens. roman. Indexer. 2. 4. countryman. 1. 2. Inverted index. 16. 13. Go Over.

574 views • 50 slides

Lecture 4: Dictionaries and tolerant retrieval

Web Search and Mining. Lecture 4: Dictionaries and tolerant retrieval. Recap of the previous lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems: Hyphens, apostrophes, compounds, Chinese Term equivalence classing:

657 views • 49 slides

Tolerant retrieval

657 views • 49 slides

Lecture 3: Dictionaries and tolerant retrieval Related to Chapter 3:

588 views • 49 slides

Modified from Stanford CS276 slides Chap. 3: Dictionaries and tolerant retrieval

Modified from Stanford CS276 slides Chap. 3: Dictionaries and tolerant retrieval. Ch. 2. Recap of the previous lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems: Hyphens, apostrophes, compounds, Chinese Term equivalence classing:

625 views • 49 slides

Lecture 4: Dictionaries and tolerant retrieval

Web Search and Mining. Lecture 4: Dictionaries and tolerant retrieval. Recap of the previous lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems: Hyphens, apostrophes, compounds, Chinese Term equivalence classing:

495 views • 49 slides

CS276A Information Retrieval

587 views • 48 slides

CS276

CS276. Lecture 3 Dictionaries and Tolerant retrieval. Recap of the previous lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems: Hyphens, apostrophes, compounds, Chinese Term equivalence classing:

674 views • 48 slides

CS276A Information Retrieval

590 views • 48 slides

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan

CS276: Information Retrieval and Web Search Christopher Manning and Prabhakar Raghavan Lecture 3: Dictionaries and tolerant retrieval. Ch. 2. Recap of the previous lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems:

639 views • 48 slides

496 views • 48 slides

CS276: Information Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan

CS276: Information Retrieval and Web Search Pandu Nayak and Prabhakar Raghavan Lecture 3: Dictionaries and tolerant retrieval. Ch. 2. Recap of the previous lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems:

494 views • 48 slides

Tolerant IR

573 views • 47 slides

CES 514 Data Mining Feb 17, 2010

CES 514 Data Mining Feb 17, 2010 Lecture 3: The dictionary and tolerant retrieval. Ch. 2. Recap of the previous lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems: Hyphens, apostrophes, compounds, Chinese

540 views • 43 slides

Dictionaries and Tolerant retrieval

Dictionaries and Tolerant retrieval. Slides adapted from Stanford CS297:Introduction to Information Retrieval. A skipped lecture. The type/token distinction Terms are normalized types put in the dictionary Tokenization problems: Hyphens, apostrophes, compounds, Chinese

415 views • 39 slides

Dynamic programming algorithms for all-pairs shortest path and longest common subsequences

837 views • 37 slides

A method for unsupervised broad-coverage lexical error detection and correction

A method for unsupervised broad-coverage lexical error detection and correction. 4th Workshop on Innovative Uses of NLP for Building Educational Applications Workshop NAACL June 5, 2009 Nai-Lung Tsao and David Wible National Central University, Taiwan. The Research Context.

285 views • 28 slides

Hamming Distance

736 views • 9 slides

Minimum Edit Distance

Minimum Edit Distance. Weighted Minimum Edit Distance. Weighted Edit Distance. Why would we add weights to the computation? Spell Correction: some letters are more likely to be mistyped than others Biology: certain kinds of deletions or insertions are more likely than others.

209 views • 7 slides