1 / 11

Generative and Discriminative Models in Text Classification

Generative and Discriminative Models in Text Classification. David D. Lewis Independent Consultant Chicago, IL, USA Dave@DavidDLewis.com www.DavidDLewis.com Workshop on Challenges in Information Retrieval and Language Modeling, U Mass, CIIR, Amherst, MA, 11 Sept 2002 . Text Classification.

dee
Download Presentation

Generative and Discriminative Models in Text Classification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Generative and DiscriminativeModels in Text Classification David D. Lewis Independent Consultant Chicago, IL, USA Dave@DavidDLewis.com www.DavidDLewis.com Workshop on Challenges in Information Retrieval and Language Modeling, U Mass, CIIR, Amherst, MA, 11 Sept 2002

  2. Text Classification • Given a document, decide which of several classes it belongs to: • TREC filtering • TDT tracking task • Text categorization! • Automated indexing, content filtering, alerting,... • More LM papers here than any other IR problem • Others: parts of IE, author identification,...

  3. Lang. Models are Generative • Model predicts probability document d will be generated by a source c • e.g. Unigram language model: • Parameters, i.e. P(w|c)’s, are fit to optimally predict generation of d

  4. Classify Text w/ Gen. Model • One source model for each class c • Choose class c with largest value of: • For 2 classes, unigram P(d|c), we have: • aka Naive Bayes (NB), Roberston/KSJ

  5. The Discriminative Alternative • Directly model probability of generating class conditional on words: P(c|w) • Logistic regression: • Tune parameters to optimize conditional likelihood (class probability predictions)

  6. LR & NB: Same Parameters!

  7. Observations • LR & NB have same parameterization for 2- or k-class, binary or raw TF weighting • LR outperforms NB in text categorization and batch filtering studies • NB optimizes parameters to predict words, LR optimizes to predict class

  8. False Hopes for LM? • Leveraging unlabeled data (e.g. EM)? • Initial results show only small impact (same story as syntactic class tagging) • Non-unigram models • More accurately predict the wrong thing? • Cross-lingual TC • Any more than MT followed by TC?

  9. True LM Hopes 1: Small Data? • Number training examples to reach maximum effectiveness (Ng & Jordan ‘01): • NB: O(log # features) • LR: O(# features) • LR and NB not compared yet (?) in low data (TREC adaptive, TDT tracking) case • Priors/smoothing likely to prove critical

  10. True LM Hopes 2: Facets? • MeSH category assignments: Anti-Inflammatory Agents, Non-Steroidal/*therapeutic use Tumor Necrosis Factor/antagonists & inhibitors/immunology • Most combinations have zero training data • Berger & Lafferty MT approach?

  11. Non-LM TC Challenges? • Integration of prior knowledge • Choosing documents to label (TREC adaptive, active learning, sampling) • Combining text and nontext predictors • Knowing how well a classifier will/can do • Evolving category systems, switching vocabularies

More Related