Course: Computational Linguistics

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Topic outline

Computational Linguistics

Instructors: Assoc. Prof. Dr Balisoamanandray Ranaivo-Malançon & Suhaila bt Saee

Course Synopsis

This set of slides provides an overview of the area of Computational Linguistics (CL) and Natural Language Processing (NLP). It is intended for Undergraduate Students. No prior knowledge in computer programming is required but having that kind of skill can help to explore some subjects in greater depth. The topics covered the basic techniques for text processing. Students will learn word processing (tokenisation, computational morphology, part of speech tagging, computational lexical semantics, and named entity recognition), sentence processing (syntactic parsing and semantic parsing). Two applications, the oldest application, that is machine translation, and the newest application, that is, NLP for social media, illustrate the implementation of the text processing techniques.

This OpenCourseWare@UNIMAS and its related materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
- Additional notes File
This topic
Topic 1: Introduction
A clarification of the terms "Computational Linguistics", "Natural Language Processing", and "Language Technology".

An overview of the characteristics of natural languages and the scientific field that studies them, that is, linguistics.

An overview of the common methods used in Computational Linguistics and Natural Language Processing.

A list of open-source tools for processing natural languages.
- Introduction File
Topic 2: Tokenisation
Clarification of the term "word".

Definition of tokenisation.

Problems and solutions in tokenisation.

Existing tools for tokenisation.
- Tokenisation File
Topic 3: Computational Morphology
An overview of computational morphology and morphology.

Current issues and methods in computational morphology.

Two subfields in computational morphology: lemmatisation and stemming.

Existing tools for morphological analyser, lemmatiser and stemmer.
- Computational Morphology File
Topic 4: POS Tagging
An overview of part-of-speech (POS) tagging.

Current issues and methods apply in POS tagging.

POS taggers evaluation methods.

Existing tools for POS tagging.
- POS Tagging File
Topic 5: Syntactic Parsing
- Overview of constituency grammars and dependency grammars.
- Explanation and illustration of four syntactic parsing methods:
- Syntactic Parsing File
Topic 6
Computational Lexical Semantics

Short introduction to the linguistic background of computational lexical semantics: lexical semantics, lexicon, lexeme, lemma, sense, and lexical ambiguities.

Explanation and illustration of the different lexical relations that exist between lexemes.

Presentation of some lexical semantics resources.

Brief introduction of word sense disambiguation.
- Computational Lexical Semantics File
Topic 7: Semantic Parsing
Definition of semantic parsing.

Illustration of the application of semantic parsing in question-answering system.

Explanation and illustration of some semantic representations:

First order predicate calculus

Semantic networks

Frames

Explanation and illustration of syntax-driven semantic parsing.

Short introduction to shallow semantic parsing (or semantic role labelling).
- Semantic Parsing File
Topic 8: Named Entity Recognition
Definitions: named entity recognition, named entity, mention, gazetteer.

Brief introduction of coreference resolution.

Role of named entity recognition in information extraction system.

Named entity recognition issues.

Named entity recognition models.

Named entity recognition methods.

Named entity recognition evaluation metrics.
- Named Entity Recognition File
Topic 9: Machine Translation
Definitions of translation and machine translation (MT).

MT classification.

Issues and methods in MT.

Alignment in MT.

Current and future directions of MT.
- Machine Translation File
Topic 10: Natural Language Processing for Social Media
An introduction to NLP for social media.

Relationship between NLP and social media.

Issues in NLP when dealing with social media.

Example of NLP application in social media: recommendation system.
- Natural Language Processing for Social Media File

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