Text Representation

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Text Representation Text Classification for
Intelligent Information Retrieval

• Ning Yu
• School of Library and Information Science
• Indiana University at Bloomington

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Outline

• The big picture
• A specific problem opinion detection

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Intelligent information retrieval

• Characteristics
• Not restricted to keyword matching and Boolean
  search
• Deal with natural language query and advanced
  search criteria
• Coarse-to-fine level of granularity
• Automatically organize/evaluate/interpret
  solution space
• User-centered, e.g., adapt to users learning
  habit
• Etc.

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Intelligent information retrieval

• System Preferences
• Various source of evidence
• Natural language processing
• Semantic web technologies
• Automatic text classification
• Etc.

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Intelligent IR system diagram
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A Specific QuestionSemi-Supervised Learning
for Identifying Opinions in Web Content

• Dissertation work

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Growing demand for online opinions

• Enormous body of user-generated content
• About anything, published anywhere and at any
  time
• Useful for literature review, decision making,
  market monitoring, etc.

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Major approaches for opinion detection
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Whats Essential?Labeled Data! And lots of
them!!!

• To acquire a broad and comprehensive collection
  of opinion-bearing features (e.g., bag-of-words,
  POS words, N-grams (ngt1), linguistic
  collocations, stylistic features, contextual
  features)
• To generate complex patterns (e.g., good
  amount) that can approximate the context of
  words.
• To generate and evaluate opinion detection
  systems
• To allow evaluation of opinion detection
  strategies with high confidence

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Challenges for opinion detection

• Shortage of opinion-labeled data manual
  annotation is tedious, error-prone and difficult
  to scale up Domain transfer strategies designed f
  or opinion detection in one data domain generally
  do not perform well in another domain

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Motivations research question

• Easy to collect unlabeled user-generated content
  that contains opinions
• Semi-Supervised Learning (SSL) requires only a
  limited number of labeled data to automatically
  label unlabeled data has achieved promising
  results in NLP studies
• Is SSL effective in opinion detection both in
  sparse data situations and for domain adaptation?

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Datasets data split
Dataset (sentences) Blog Posts Movie Reviews News Articles
Opinion 4,843 5,000 5,297
Non-opinion 4,843 5,000 5,174
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Two major SSL methods Self-training

• Assumption Highly confident predictions made by
  an initial opinion classifier are reliable and
  can be added to the labeled set.
• Limitation Auto-labeled data may be biased by
  the particular opinion classifier.

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Two major SSL methods Co-training

• Assumption Two opinion classifiers with
  different strengths and weaknesses can benefit
  from each other.
• Limitation It is not always easy to create two
  different classifiers.

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Experimental design

• General settings for SSL
• Naïve Bayes classifier for self-training
• Binary values for unigram and bigram features
• Co-training strategies
• Unigrams and bigrams (content vs. context)
• Two randomly split feature/training sets
• A character-based language model (CLM) and a
  bag-of-words model (BOW)

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Results Overall

• For movie reviews and news articles, co-training
  proved to be most robust
• For blog posts, SSL showed no benefits over SL
  due to the low initial accuracy

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Results Movie reviews

• Both self-training and co-training can improve
  opinion detection performance
• Co-training is more effective than self-training

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Results Movie reviews (cont.)

• The more different the two classifiers, the
  better the performance

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Results Domain transfer

(movie
reviews-gtblog posts)

• For a difficult domain (e.g., blog), simple
  self-training alone is promising for tackling the
  domain transfer problem.

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Contributions

• Comprehensive research expands the spectrum of
  SSL application to opinion detection
• Investigation of SSL model that best fits the
  problem space extends understanding of opinion
  detection and provides a resource for
  knowledge-based representation
• Generation of guidelines and evaluation baselines
  advances later studies using SSL algorithms in
  opinion detection
• Research extensible to other data domains,
  non-English texts, and other text mining tasks

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Thank you!
If you want a second opinion, Ill ask my
computer
www.CartoonStock.com
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