Person Name
Disambiguation by
Bootstrapping
Presenter: Lijie Zhang
Advisor: Weining Zhang
Outlines
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Introduction
Motivation
Two-stage Clustering Algorithm
Experiments
People Name Disambiguation


Given a target name (query q ), search engine
returns a set of web pages P={d1, d2, …, dn }
Task: cluster web pages P such that each
cluster refers to a single person.
Example: People Name Disambiguation
People Name Disambiguation
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A typical solution:
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Extract a set of features from each document returned by search
engine
Cluster the documents based on some similarity metrics on sets
of features
Two types of features
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Strong features such as named entities (NEs), compound key
words (CKWs), URLs
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NE: Paul Allen, Microsoft (indicate the person Bill Gates)
CKW: chief software architect (a concept strongly related to Bill
Gates)
Very strong ability to distinguish between clusters.
Weak features: single words
People Name Disambiguation
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Evaluation Metric: F measure
 Treat
each cluster as if it were the result of a
query and each class as if it were the desired
set of documents for a query
 For
class i and cluster j,
Recall(i, j)= nij/ni, Precision(i, j)=nij/nj
 F(i, j) = (2 * Recall(i, j) * Precision(i, j)) /
((Precision(i, j) + Recall(i, j))
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Motivation
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Problem of current systems: Using only strong
features achieves high precision but low recall.
Proposed solution: two-stage clustering
algorithm by bootstrapping to improve the recall
value.
 1st stage: strong features
 2nd stage: weak features
Two-stage Clustering Algorithm
Input: one query string
 Output: a set of clusters
1. Preprocessing documents returned by
search engine
2. First-stage clustering
3. Second-stage clustering
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Preprocessing a Document
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Covert HTML files to text files
 Remove
HTML tags
 Keep sentences

Extract text around query string
 Using

a window size
Extract strong features (NEs, CKWs,
URLs)
Extract Strong Features
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Use Stanford NER to identify NEs:
a
set of sets of names including names of persons,
organizations, and places

Compound Key Word (CKW) Features: a set of
CKWs
 Extract compound words (CW): w1w2..wl
 Score each CW:
 Determine CKW based on a threshold of

scores.
Extract URLs from the original HTML files
 exclude
URLs with high frequencies
Two-stage Clustering Algorithm
Input: one or more query strings
 Output: a set of clusters
1. Preprocessing documents returned by
search engine
2.1st stage clustering
3. 2nd stage clustering

First stage clustering
1.
2.
Calculate the similarities between documents
based on these features
Use standard hierarchical agglomerative
clustering (HAC) algorithm for clustering
Document Similarities
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Similarity for NE features and CKW
features

avoids too small denominator values in the
equation
Document Similarities
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Similarity for URLs
Document Similarities
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Similarity for NE:
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Similarities for NE, CKW, and URL
First stage clustering
1.
2.
Calculate the similarities between documents
based on these features
Use standard hierarchical agglomerative
clustering (HAC) algorithm for clustering
HAC algorithm
Starts from one-in-one clustering, i.e. each
document is a cluster
 Iteratively merge the most similar cluster
pairs, which similarity is above a threshold.
 Cluster similarity:

Two-stage Clustering Algorithm
Input: one or more query strings
 Output: a set of clusters
1. Preprocessing documents returned by
search engine
2.1st stage clustering
3. 2nd stage clustering
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Second Stage Clustering
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Goal: Cluster documents still in one-in-one clustering
after the first stage clustering
Idea of bootstrapping algorithm:
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Given some seed instances, finds patterns useful to extract such
seed instances;
Use these patterns to harvest new instances, and form the
harvested new instances new patterns are induced.
Instances correspond to documents
Patterns correspond to weak features: 1-gram, 2-gram in
experiment
Second Stage Clustering
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Experiments Setup
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Dataset: WePS-2
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30 names, each has 150 pages
The same page can refer to two or more entities;
Evaluation Metrics [5]
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Multiplicity precision and recall between document e and e’
C(e) is predicted cluster of e, L(e) is the cluster assigned to e by
the gold standard
Example of Evaluation Metrics
L(1)={A,B} L(2)={A,B}
C(1)={ct1, ct2}
C(2)={ct1, ct2}
L(1)={A,B} L(2)={A,B}
C(1)={ct1} C(2)={ct1,
ct2}
L(1)={A,B} L(2)={A,B}
C(1)={ct1,ct2,ct3}
C(2)={ct1, ct2,ct3}
Experiments Setup
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Evaluation Metrics
 Extended
B-Cubed precision (BEP) and recall (BER)
Experiments Setup
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Baselines:
 First
stage clustering: all-in-one, one-in-one,
combined baseline (each doc belongs to one
cluster from all-in-one and one from one-inone).
 Second stage clustering: TOPIC algorithm,
CKW algorithm
Experiments Results
References
[1] A. Bagga and B. Baldwin. Entity-based cross-document coreferencing using the
vector space model. In Proceedings of COLING-ACL 1998, pages 79–85, 1998.
[2] C. Niu, W. Li, and R. K. Srihari. Weakly supervised learning for cross-document
person name disambiguation supported by information extraction. In Proceedings of
42nd Annual Meeting of the Association for Computational Linguistics (ACL-2004),
pages 598–605, 2004.
[3] X. Liu, Y. Gong, W. Xu, and S. Zhu. Document clustering with cluster refinement and
model selection capabilities. In Proceedings of the 25th annual international ACM
SIGIR conference on Research and development in information retrieval, pages 191–
198, 2002.
[4] X. Wan, M. L. J. Gao, and B. Ding. Person resolution in person search results:
WebHawk. In Proceedings of CIKM2005, pages 163–170, 2005.
[5] E. Amigo, J. Gonzalo, J. Artiles, and F. Verdejo. A comparison of extrinsic clustering
evaluation metrics based on formal constraints. Information Retrieval, 12(4), 2009.
[6] Minoru Yoshida, Masaki Ikeda, Shingo Ono, Issei Sato, Hiroshi Nakagawa. Person
Name Disambiguation by Bootstrapping. In Proceedings of SIGIR, 2010.