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Comprehensive Guide to Text Analytics Exam Preparation

This guide outlines the key areas covered in the Text Analytics module and provides insights into the type of questions that may be asked during the exam. Topics include text classification, clustering, sentiment analysis, named entity recognition, coreference resolution, relation extraction, and summarization. For each topic, we present examples of general, theoretical, and practical questions, offering a clear understanding of expectations and required knowledge. This resource is essential for effective exam preparation and mastery of text analytics concepts.

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Comprehensive Guide to Text Analytics Exam Preparation

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  1. 807 - TEXT ANALYTICS Massimo PoesioRevision Lecture

  2. THE EXAM

  3. THE QUESTIONS • Each question worth about 30% • The typical question: • A general part worth 5-6 points asking you to provide a definition (e.g., ‘what is named entity recognition’) / examples of applications of a TA technique • A more theoretical question (‘how is PMI defined?’) • A question in which you have to demonstrate you understand the concepts by putting it into practice

  4. TOPICS COVERED IN THE MODULE • Preprocessing • Lab: GATE • Text classification • Clustering • Sentiment analysis • Named entity recognition • D2W • Coreference • Relation extraction • Summarization

  5. TEXT CLASSIFICATION • What is text classification • Methods • Naïve Bayesvs Decision Trees (also: SVM in the labs) • Feature selection (PMI, Information Gain … ) • Applications • Spam detection

  6. WHAT KIND OF QUESTIONS YOU MAY EXPECT • General questions: what is text classification? • Examples of theoretical question: • Explain how decision tree are learned • Explain what is information gain • Practical question • Show how text classification could be used in a practical application • Apply some method in a toy example

  7. CLUSTERING • What is clustering • Methods: • The vector space model • K-Means, Agglomerative clustering

  8. WHAT KIND OF QUESTIONS YOU MAY EXPECT • Examples of general questions: • What are the uses of text clustering? • Examples of theoretical questions: • Explain the K-means algorithm • What is TF/IDF • Examples of practical questions: • Show how clustering could be used in a practical application • Apply some method (e.g., agglomerative clustering) in a toy example (e.g., the question gives you a few vectors)

  9. SENTIMENT ANALYSIS • What is sentiment analysis • Approaches: • Supervised vs. Lexically-based • Levels of granularity • Resources • Sentiment lexica, etc

  10. WHAT KIND OF QUESTIONS YOU MAY EXPECT • Examples of general questions: • Give two examples of use of sentiment analysis • Examples of theoretical questions: • Explain what is aspect-based sentiment analysis • Give examples of words with positive and negative polarity • Examples of practical questions: • Apply text classification techniques to develop a sentiment analyzer for a particular company / product

  11. NAMED ENTITY RECOGNITION • The supervised approach to NER • NER as a classification problem • The IOB format • Sequence labelling • Features most used • The semi-supervised approach • Distant learning

  12. WHAT KIND OF QUESTIONS YOU MAY EXPECT • Examples of general questions: • What is NER and what issues you have to deal with • Examples of theoretical questions: • Explain how the IOB notation works • What kind of machine learning approach works best with NER and why • Examples of practical questions: • Explain how you could develop a NER system for a non-standard domain

  13. USING SOCIALLY CREATED RESOURCES FOR TEXT ANALYTICS • Wikipedia • What information is in Wikipedia • Wikipedia for text classification • Disambiguation to Wikipedia • Wikipedia for NER / coreference • Open Mind commonsense • Games-with-a-purpose

  14. WHAT KIND OF QUESTIONS YOU MAY EXPECT • Examples of general questions: • Why are large-scale resources needed in text analytics and what is available • Examples of theoretical questions: • Types of information in Wikipedia • What is distant learning and how does it work • Examples of practical questions: • Suppose you need a large scale resource for <domain X>. What are your options?

  15. COREFERENCE • Issues in coreference • E.g., main problems with identifying mentions • Theoretical ideas: • The Vilain et al evaluation algorithm • The Soon et al algorithm • Applications:

  16. WHAT KIND OF QUESTIONS YOU MAY EXPECT • Examples of general questions: • What kind of problems does a coreference resolver encounter? • Examples of theoretical questions: • Explain (some aspect of) the Soon et al algorithm • How is precision computed in the Vilain et al evaluation algorithm • Examples of practical questions: • Show how the Soon et al algorithm could be used in a practical application • Compute the MUC score in a toy example

  17. RELATION EXTRACTION • The supervised approach to relation extraction • E.g., ACE-style relations • The semi-supervised approach to relation extraction • E.g., KNOW-IT-ALL

  18. SUMMARIZATION • Types of summarization • Extractive vs abstractive, single vsmulti document • Extractive summarization: ranking sentences • Discourse structure in summarization • Evaluation • ROUGE • Pyramids

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