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- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
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- School of Life Science and Technology
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School of Environment and Society
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- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- School of Computing
- Instructor(s)
- Teufel Simone
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon3-4(H119A) Thr3-4(H119A)
- Group
- -
- Course number
- XCO.T673
- Credits
- 2
- Academic year
- 2017
- Offered quarter
- 2Q
- Syllabus updated
- 2017/4/19
- Lecture notes updated
- 2017/7/31
- Language used
- English
- Access Index
-
Course description and aims
Lexical semantics is about how we can describe the meaning of words -- what does a word mean on its own, and how does the word's meaning change when we combine it with others. Word meaning is central to the area of Natural Language Processing; all modern NLP applications require knowledge of lexical semantics, and rely on the outcome of previous lexical semantics processing.
Word meaning might appear as self-explanatory, but it is not. As computational linguists, our goal is often to determine meaning differences automatically, and to apply them in many applications. This course shows how we can profit enormously from the relevant theoretical and practical approaches in Linguistics and Computational Linguistics. This course carries together such relevant approaches, and provides an overview of the area of lexical semantics. It treats lexical properties of words such as their subcategorisation, their exact word sense, and others. The questions in lexical semantics treated here are well-known, but are rarely taught in the form of a specialised course that aims to be theoretical thorough but also enable students to build better-informed and semantically more precise NLP applications.
The style of the class is lectures mixed with students' own annotation of sample texts. Students perform homework annotations in most classes to experience by themselves the phenomena treated each week.
Student learning outcomes
At the end of the course students should be able to
(a) explain what "lexical semantics" is and its relationship to natural language processing
(b) explain several phenomena in lexical semantics treated in detail and appreciate their difficulty through their own annotation of textual material (homework)
(c) explain and understand computational approaches to each of the lexical semantics areas considered in detail
(d) be able to design and implement a sample application based on the methods above.
Specialist skills will be developed. Communication skills will be developed as well, because the course language is English and because student presentations are part of the assessment. Critical skills will also be developed, as the textbook reading is supplemented by research articles.
Keywords
word sense, word sense disambiguation, lexical semantics, subcategorisation frame, sentiment detection, figurative language, entailment, pragmatics
Competencies that will be developed
| ✔ Specialist skills | ✔ Intercultural skills | Communication skills | Critical thinking skills | Practical and/or problem-solving skills |
Class flow
Specified in the class
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Overview | Specified in the class |
| Class 2 | Word senses -- the phenomena | |
| Class 3 | Word sense disambiguation algorithms (symbolic) | |
| Class 4 | Word sense disambiguation algorithms (ML) and evaluation | |
| Class 5 | WordNet, Lexical Chains and Learning of WN relations | |
| Class 6 | Lexical semantics of verbs | |
| Class 7 | Subcategorisation frame acquisition and selectional restrictions | |
| Class 8 | Lexical semantics of adjectives | |
| Class 9 | Adjective-based sentiment detection | |
| Class 10 | Lexical semantics of nouns and Noun-Noun relations | |
| Class 11 | Figurative Language 1 -- Metonymy | |
| Class 12 | Figurative Language 2 -- Metaphor | |
| Class 13 | Entailment and the RTE task | |
| Class 14 | Introduction to Pragmatics; Presuppositions | |
| Class 15 | Wrap-up and Outlook |
Textbook(s)
Background reading (papers) will be announced ahead of each lecture on the website.
Reference books, course materials, etc.
Jurafsky and Martin, Speech and Language Processing, 2nd edition, 2008.
Assessment criteria and methods
10 % Contribution to Class discussion.
20 % Annotation Homework.
70 % Literature-based Essay or Report on Student-Designed Experiment.
Related courses
- ART.T459 ： Natural Language Processing
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Programming Ability.
Good English ability (in order to appreciate subtle details)
Completion of Course "Natural Language Processing" is preferable.
