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Extracting Key Terms From
Noisy and Multi-theme Documents
Maria Grineva, Maxim Grinev and Dmitry Lizorkin
Institute for System Programming of RAS
Outline
1. Key terms extraction: traditional approaches and
applications
2. Using Wikipedia as a knowledge base for Natural
Language Processing
3. Main techniques of our approach:
• Wikipedia-based semantic relatedness
• Network analysis algorithm to detect community
structure in networks
4. Our method
5. Experimental evaluation
Key Terms Extraction
• Basic step for various NLP tasks:
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document classification
document clustering
text summarization
inferring a more general topic of a text document
• Core task of Internet content-based advertising
systems, such as Google AdSense and Yahoo! Contextual
Match
– Web pages are typically noisy (side bars/menus, comments,
future announces, etc.)
– Dealing with multi-theme Web pages (portal home pages, etc.)
Approaches to Key Terms Extraction
• Based on statistical learning:
– use for example: frequency criterion (TFxIDF model),
keyphrase-frequency, distance between terms normalized by
the number of words in the document (KEA)
– compute statistical features over Wikipedia corpus (Wikify! )
– require training set
• Based on analyzing syntactic or semantic term
relatedness within a document
– compute semantic relatedness between terms (using, for
example, Wikipedia)
– modeling document as a semantic graph of terms and
applying graph analysis techniques to it (TextRank)
– no training set required
Using Wikipedia as a Knowledge Base for
Natural Language Processing
• Wikipedia (www.wikipedia.org) – free open
encyclopedia
– Today Wikipedia is the biggest encyclopedia (more
than 2.7 million articles in English Wikipedia)
– It is always up-to-date thanks to millions of editors
over the world
– Has huge network of cross-references between
articles, large number of categories, redirect pages,
disambiguation pages => rich resource for
bootstrapping NLP and IR tasks
Basic Techniques of Our Method:
Semantic Relatedness of Terms
• Semantic relatedness assigns a score for a pair of
terms that represents the strength of relatedness
between the terms
• We use Wikipedia compute terms semantic
relatedness
• We use semantic relatedness to model document
as a graph of terms
Basic Techniques of Our Method:
Semantic Relatedness of Terms
• Wikipedia-based semantic relatedness for the two terms can
be computed using:
– the links found within their corresponding Wikipedia articles
– Wikipedia categories structure
– the article’s textual content
• Using Dice-measure for Wikipedia-based semantic relatedness
Basic Techniques of Our Method:
Detecting Community Structure in Networks
• We discover terms communities in a document graph
• Community – densely interconnected group of nodes in a
network
• Girvan-Newman algorithm for detection community
structure in networks:
• betweenness – how much is edge
“in between” different communities
• modularity - partition is a good one,
if there are many edges within
communities and only a few
between them
Our Method
1. Candidate terms extraction
2. Word sense disambiguation
3. Building semantic graph
4. Discovering community structure of the semantic
graph
5. Selecting valuable communities
Our Method:
Candidate Terms Extraction
• Goal: extract all terms from the document and for
each term prepare a set of Wikipedia articles that can
describe its meaning
• Parse the input document and extract all possible ngrams
• For each n-gram (+ its morphological variations)
provide a set of Wikipedia article titles
– “drinks”, “drinking”, “drink” => [Wikipedia:] Drink; Drinking
Our Method:
Word Sense Disambiguation
• Goal: choose the most appropriate Wikipedia article from the set of
candidate articles for each ambiguous term extracted on the previous
step
• Use of Wikipedia disambiguation and redirect pages to obtain
candidate meanings of ambiguous terms
Denis Turdakov, Pavel Velikhov
“Semantic Relatedness Metric for Wikipedia Concepts Based on
Link Analysis and its Application to Word Sense Disambiguation”
SYRCoDIS, 2008
Our Method:
Building Semantic Graph
• Goal: building document semantic graph using semantic
relatedness between terms
Semantic graph built from a news article
"Apple to Make ITunes More Accessible For the Blind"
Our Method:
Detecting Community Structure of the Semantic Graph
Our Method:
Selecting Valuable Communities
• Goal: rank term communities in a way that:
– the highest ranked communities contain key terms
– the lowest ranked communities contain not important terms,
and possible disambiguation mistakes
• Use:
– density of community – sum of inner edges of community
divided by the number of vertices in this community
– informativeness – sum of keyphraseness measure
(Wikipedia-based TFxIDF analogue) of community terms
• Community rank: density*informativeness
Our Method:
Selecting Valuable Communities
• In 73% of web pages decline in communities scores
separates key-terms communities from non-important ones
Advantages of the Method
• No training. Instead of training the system with handcreated examples, we use semantic information derived
from Wikipedia
• Noise and multi-theme stability. Good at filtering out
noise and discover topics in Web pages
• Thematically grouped key terms. Significantly improve
further inferring of document topics using, for example,
spreading activation over Wikipedia categories graph
• High accuracy. Evaluated using human judgments (further
in this presentation)
Experimental Evaluation on Noise-free dataset
• Classical – TFxIDF, Yahoo! Terms Extractor
• Wikipedia-based – Wikify!, TextRank
• Evaluation on noise-free dataset (blog posts) using human
judgment
Experimental Evaluation on Web Pages
• Performance of our method on different kinds of Web pages
• Comparison to other methods
Experimental Evaluation on Web Pages
• Multi-theme stability evaluated on compound Web
pages (popular news site, portal homepages, etc.)
Thank You!
Any Questions?
Email
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