Resources for LIS 9732/9832

Course Information:

LIS9732/LIS9832 Language and Computer Technologies for Libraries and Beyond

Relationship to the Goals and Objectives of the MLIS Program

Students who complete this course will be able to:

  1. Critically evaluate natural language technologies and envisage their creative applications in library settings and digital media at large;
  2. Identify, select, acquire, organize, describe, and provide access to information in a textual format;
  3. Employ appropriate natural language technologies to organize textual informationin a meaningful way.

Course Description

Introduction to linguistic and computational aspects of natural language processing technologies. Familiarity with underlying principles and techniques required to perform all levels of language understanding and processing of naturally occurring text. Critical assessment of the use of language technologies in a variety of applications.

PREREQUISITE: Knowledge of spreadsheets, presentation software, database software, and some HTML coding. When in doubt, Contact the Professor.

Course Objectives

  1. To gain an awareness and appreciation of the complexity of natural language.
  2. To analyze the research literature on linguistic and computational aspects of natural language processing techniques.
  3. To critically evaluate a variety of applications that use natural language processing technologies
  4. To connect Natural Language Processing (NLP) technologies and library applications in an innovative way
  5. For LIS 9832 (Optional for LIS 9732): To gain practical experience in basic text analysis with NLP techniques and/or in advanced NLP applications.

Sample Content
  1. Computing with Words
  2. Phonetics: Speech. Sound structure. Phoneme. Classifications. Statistical vs Symbolic NLP.
  3. Corpus Linguistics: Collocations. Concordances. Annotation.
  4. Lexicology: Corpora. Lexicons. WordNet.
  5. Morphology: Components of Words. Informative Affixes. Stemming and Lemmatizing.
  6. Part-of-Speech Tagging: Challenges. Approaches. Accuracy.
  7. Parsing: Phrase Structures. Context Free Grammars. Methods.
  8. Semantic Networks. Thematic Roles. Frames. Case Grammars. Conceptual Graphs.
  9. Discourse: Cohesion. Anaphora. Co-reference resolution. Discourse Structure. Sublanguage.
  10. Pragmatics: Speech Act Theory. Gricean Maxims. Dialogues. Plan Recognition. Subjectivity.
  11. Final Thoughts: Myths and Reality.

  12. Applications:

  13. Machine Translation: Automated Summarization. Questions and Answering
  14. Natural Language Interaction: Dialogue Systems. Chatbots. Speech Recognition.
  15. Mining Content of Social Software Sites. Automatic Indexing. Information Extraction. Text Mining.
  16. Computer Assisted Language Learning. Language Identification. Terminology Alignment and Comprehension Aids.

Meet ELIZA, an intelligent agent that simulates natural language conversation. This is what systems were capable of in 1966.

What are "intelligent programs" capable of doing today? Have you followed the battle of the IBM Deep Questions-Answering machine, WATSON, with Jeopardy contestants? What kind of roles will they perform in the future? What's a myth and what's the reality?

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