Difference between revisions of "J. Artiles et al. EMNLP 2009"
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if they share the same cluster or not coreferent, Otherwise. | if they share the same cluster or not coreferent, Otherwise. | ||
| − | The major contribution of this paper is to introduce Maximal Pairwise Accurary measure | + | The major contribution of this paper is to introduce Maximal Pairwise Accurary (MPA) measure |
that is an upper bound score for a combination of features | that is an upper bound score for a combination of features | ||
regardless of the underlying machine learning algorithms used and parameter settings. | regardless of the underlying machine learning algorithms used and parameter settings. | ||
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such as local, global and snippet tokens, n-grams, etc. | such as local, global and snippet tokens, n-grams, etc. | ||
# results are sensitive to the NER system used. | # results are sensitive to the NER system used. | ||
| + | |||
| + | == MPA == | ||
| + | Given a feature set <math> X = {x_{1}, x_{2}, \dots, x_{n} }</math> | ||
| + | The intuition of this score is that if at least one feature gives correct information, then the perfect algorithm | ||
| + | would produce a correct output. | ||
Revision as of 01:10, 31 October 2010
Contents
Citation
Javier Artiles, Enrique Amigó & Julio Gonzalo, The role of named entities in web people search, in EMNLP 2009
Online version
The role of named entities in web people search
Summary
This paper tries to determine the role of a number of features on solving Web People Search clustering problem. The paper focused on the role of NE in this task. In order to compare different features, they reformulated this clustering problem into a classification problem such that each pair of documents will be classified as coreferent if they share the same cluster or not coreferent, Otherwise.
The major contribution of this paper is to introduce Maximal Pairwise Accurary (MPA) measure that is an upper bound score for a combination of features regardless of the underlying machine learning algorithms used and parameter settings.
For experiments, they used two standard datasets for Web People Search Systems: WePS-1 and WePS-2. They concluded
- NEs do not improve the clustering when compared with a combination of simpler features
such as local, global and snippet tokens, n-grams, etc.
- results are sensitive to the NER system used.
MPA
Given a feature set The intuition of this score is that if at least one feature gives correct information, then the perfect algorithm would produce a correct output.
