| Natural language processing (NLP) is a broad and exciting field at the intersection of computer science, formal linguistics and cognitive science. Areas include machine translation, information retrieval, document processing and summarization, machine learning, grammar formalisms and parsing algorithms. This course will survey selected topics in NLP highlighting both the use of large-scale lexical resources and techniques in the implementation of linguistic theories. |
Instructor: Sandiway Fong sandiway@email.arizona.edu
Office (temporary): RM 309 Douglass
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| Date | Lecture Notes and Papers | Topic | |
|---|---|---|---|
| Powerpoint | |||
| 1/15 | lecture1.pdf | lecture1.ppt | Background and organizational meeting.
For next time: WordNet. Read
Introduction to WordNet: An On-line Lexical Database
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| 1/20 | lecture2.pdf | lecture2.ppt | 6 slides. Introduction to WordNet. |
| 5papers.pdf | Reading: WordNet 5 core papers.
Available locally or download directly from Princeton http://www.cogsci.princeton.edu/~wn/. |
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| 1/22 | lecture3.pdf | lecture3.ppt | WordNet software: browsers and wnconnect.
Example puzzles and possible software projects: (1) What do the following have in common? (2) Autoantonyms or antagonyms. |
| 1/27 | lecture4.pdf | lecture4.ppt | WordNet organization. Case study: verbs.
Paper: English Verbs as a Semantic Net, Fellbaum, C.
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| 1/29 | No lecture today | ||
| Date | Lecture Notes and Papers | Topic | |
|---|---|---|---|
| Powerpoint | |||
| 2/03 | lecture5.pdf | lecture5.ppt | Instructions for class presentation starting 2/17.
Paper selection from conference proceedings. |
| icos3.pdf | Semantic Opposition and WordNet | ||
| 2/05 | lecture6.pdf | lecture6.ppt | Paper assignments |
| bleaching.pdf | Semantic Bleaching and WordNet | ||
| 2/10 | telicroles.pdf | Telic Roles and WordNet | |
| 2/12 | lecture8.pdf | lecture8.ppt | WordNet and Canonical Color |
| kael.pdf | kael.ppt | Student Presentation: Kael Dai.
Paper: Automated Discovery of Telic Relations for Wordnet by De Boni and Manandhar |
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| 2/17 | lecture9.pdf | lecture9.ppt | Student Presentations |
| he.pdf | he.ppt | Hai-Feng He. Paper: Using WordNet to Improve User Modelling in a Web Document Recommender System by Magnini, B. and C. Strapparava | |
| baker.pdf | baker.ppt | Patrick Baker. Paper: Chinese Characters and Top Ontology in EuroWordNet by Wong, S. and K. Pala | |
| rabee.pdf | rabee.ppt | Rabee Ali Alshemali. Paper: Comparing Ontology-based and Corpus-based Domain Annotations in WordNet by Magnini, B. et al. | |
| chow.pdf | chow.ppt | Sandy Chow. Paper: Creating a Bilingual Ontology: A Corpus-Based Approach for Aligning WordNet and HowNet by Carpuat, M. et al. | |
| 2/19 | lecture10.pdf | lecture10.ppt | Reading list for Machine Translation
Student Presentations |
| schlecht.pdf | schlecht.ppt | Joseph Schlecht. Paper: Using Lexical Knowledge to Evaluate the Novelty of Rules Mined from Text by Basu, S. et al. | |
| alcock.pdf | alcock.ppt | Keith Alcock. Paper: Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures. by Budanitsky, A. and G. Hirst | |
| eggers.pdf | eggers.ppt | Shauna Eggers. Paper: The Informative Role of WordNet in Open-Domain Question Answering by Pasca, M. & S. Harabagiu | |
| swaminathan.pdf | swaminathan.ppt | Ranjini Swaminathan. Paper: Word Sense Disambiguation Using Semantic Graph by Unny, N. & P. Bhattacharyya | |
| 2/24 | lecture11.pdf | lecture11.ppt | Speech-to-speech Machine Translation (MT) system demo
Student Presentation |
| landis.pdf | landis.ppt | Matt Landis. Paper: Words with Attitude by Kamps, J. and M. Marx | |
| 2/26 | No lecture today | ||
| Date | Lecture Notes and Papers | Topic | |
|---|---|---|---|
| Powerpoint | |||
| 3/2 | lecture12.pdf | lecture12.ppt | Paper 1: Translation. Weaver, W.
Student Presentation |
| wing.pdf | wing.ppt | Ben Wing. Paper: Cross-Linguistic Discovery of Semantic Regularity. Peters, W. et al. | |
| 3/4 | lecture13.pdf | lecture13.ppt | Paper 3: The Mechanical Determination of Meaning. Reifler, E. |
| 3/9 | lecture14.pdf | lecture14.ppt | Paper 5: A Framework for Syntactic Translation. Yngve, V. |
| 3/11 | lecture15.pdf | lecture15.ppt | New reading list for Machine Translation
Paper 6: The Present Status of Automatic Translation of Languages. Bar-Hillel, Y. |
| 3/16 | Spring break: no lecture | ||
| 3/18 | Spring break: no lecture | ||
| 3/23 | lecture16.pdf | lecture16.ppt | Paper 12: Correlational Analysis and Mechanical Translation. Ceccato, S. |
| 3/25 | lecture17.pdf | lecture17.ppt | Paper 13: Automatic Translation: Some Theoretical Aspects and the Design of a Translation System. Kulagina, O. and I. Mel'cuk |
| 3/30 | lecture18.pdf | lecture18.ppt |
(43 slides)
Paper 16: Automatic Translation and the Concept of Sublanguage. Lehrberger, J. |
| Date | Lecture Notes and Papers | Topic | |
|---|---|---|---|
| Powerpoint | |||
| 4/1 | lecture18.pdf | lecture18.ppt |
Continued.
Paper 16: Automatic Translation and the Concept of Sublanguage. Lehrberger, J. |
| 4/6 | lecture19.pdf | lecture19.ppt |
Paper 17: The Proper Place and Men and Machies in Language Translation. Kay, M.
New readings |
| 4/8 | lecture20.pdf | lecture20.ppt |
EU News
Paper 19: Montague Grammar and Machine Translation. Landsbergen, J. |
| 4/13 | lecture21.pdf | lecture21.ppt |
Phraselator
Paper 20: Dialogue Translation vs. Text Translation. Tsujii, J,-I. and M. Nagao. Paper 21: Translation by Structural Correspondences. Kaplan, R. et al. |
| 4/15 | lecture22.pdf | lecture22.ppt |
Papero/E-Navi
Ectaco UT-103 Online translation tools story Paper 22: Pros and Cons of the Pivot and Transfer Approaches in Multilingual Machine Translation. Boitet, C. |
| 4/20 | lecture23.pdf | lecture23.ppt |
Vocera
Paper 31: A Framework of a Mechanical Translation between Japanese and English by Analogy Principle. Nagao, M. |
| 4/24 | lecture24.pdf | lecture24.ppt |
Language Weaver
Paper 33: A Statistical Approach to Machine Translation. Brown, P.F. et al. |
| 4/27 | No lecture | ||
| 4/29 | No lecture | ||
| Date | Lecture Notes and Papers | Topic | |
|---|---|---|---|
| Powerpoint | |||
| 5/4 | lecture25.pdf | lecture25.ppt |
MT Summit IX
Panel: Have we found the Holy Grail? Paper: Hutchins, J. Has Machine Translation Improved? |