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 Videoconference Room |
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| Chapter 1 |
 1.1. What is Computational Linguistics? |
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1.2. History of Computational Linguistics |
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1.3. Core Techniques |
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1.4. Areas of Computational Linguistics |
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1.5. Interdisciplinary Connections |
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 Explore NLTK packages |
import nltk
nltk.download() |
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Let's talk about computational linguistics! My experience in building Siri voices |
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The language of computational linguistics. | Walter Daelemans | TEDxAntwerp |
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Neural network in 5 mins |
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| Chapter 2 |
2.1. Word Meaning in Linguistics |
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2.2. Types of Ambiguity |
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2.3. Context and Word Meaning |
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2.4. Wordnet Introduction |
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2.5. Word Sense Disambiguation |
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Wordnet Project Homepage |
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 Chapter 2 Lecture notes |
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Word Sense Disambiguation video lecture |
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Explore Word Embedding Universe |
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| Chapter 3 |
3.1. Open versus Closed POS |
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3.2. Markov Models |
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3.3. POS Tagging with Python Libraries |
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Penn Treebank Sample |
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Chapter 3 Lecture notes |
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Markov Chains |
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Markov Chains: Generating Sherlock Holmes Stories |
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Hidden Markov Model |
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Forward Algorithm |
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Viterbi Algorithm |
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Find difference with Code Beautify |
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| Chapter 4 |
4.1. Decomposing Texts with Bag of Words Model |
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4.2. Bayesian Text Classification: The Naive Approach |
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4.3. Support Vector Machines (SVM) |
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4.4. Decision Trees |
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4.5. Neural Networks |
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Text Classification lecture notes |
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Naive Bayes, Clearly Explained!!! |
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The Kernel Trick in Support Vector Machine (SVM) |
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Decision and Classification Trees, Clearly Explained!!! |
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Recurrent Neural Networks (RNNs), Clearly Explained!!! |
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| Chapter 5 |
5.1. Constituency Relations |
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5.2. Dependency Relations |
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5.3. Treebanks |
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Universal Dependencies Project |
Step 1: Find Query UD treebanks online: Step 2: Try https://lindat.mff.cuni.cz/services/pmltq/#!/treebank/bnc/query/IYWgdg9gJgpgBAbTsAZgVzHAvHA5AOQCVc4BdIA/result/svg?filter=true&timeout=30&limit=100 To explore Vietnamese Universal Dependency Relations, start from here: https://lindat.mff.cuni.cz/services/pmltq/#!/treebank/udvi_vtb214/help
Overview of Parsing Frameworks- Analytical Tree (
a-tree) - Tectogrammatical Tree (
t-tree)
1. Analytical Tree (a-tree):- Definition: Analytical trees represent the surface syntactic structure of a sentence. They show how words are grouped into phrases and how these phrases are related to each other.
- Components:
- Pred (Predicate): The main verb of the sentence.
- Sb (Subject): The subject of the sentence.
- Obj (Object): The object of the sentence.
- AuxP (Auxiliary Preposition): Prepositions that introduce prepositional phrases.
- Atr (Attribute): Modifiers or attributes of nouns.
- Adv (Adverbial): Modifiers or complements of verbs.
- AuxC (Auxiliary Conjunction): Conjunctions that link clauses.
- AuxX: Punctuation markers like commas and periods.
The a-tree in the screenshot breaks down the sentence into its syntactic components, showing hierarchical relationships between words and phrases. 2. Tectogrammatical Tree (t-tree):- Definition: Tectogrammatical trees represent the underlying, deep syntactic structure of a sentence. They abstract away from the surface form to show more semantic and functional relationships.
- Components:
- PRED (Predicate): The main verb of the sentence.
- ACT (Actor): The logical subject or agent of the action.
- PAT (Patient): The logical object or recipient of the action.
- RSTR (Restrictive Attribute): Attributes that provide necessary information about a noun.
- MEANS: Instrumental adjuncts indicating the means by which an action is performed.
- ENUNC (Enunciation): Linking functions that relate to how the sentence is embedded in discourse.
- MANN (Manner): Adverbials expressing the manner of the action.
- APP (Apposition): Noun phrases that are in apposition to another noun phrase.
The t-tree in the screenshot abstracts away from surface syntactic categories to focus on semantic roles and deeper syntactic relationships. Comparison:Analytical Tree: - Focuses on surface syntax.
- Reflects the actual word order and grammatical relationships.
- Useful for syntactic parsing and understanding sentence structure.
Tectogrammatical Tree: - Focuses on deep syntax and semantics.
- Abstracts from surface form to show underlying relationships.
- Useful for semantic parsing and understanding the roles of different sentence elements.
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Xây dựng treebank tiếng Việt |
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Syntax & Grammar Lecture Notes |
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Constituency Parsing |
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Dependency parsing |
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| Chapter 6 |
6.1. Sources of Text |
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6.2. Sampling Strategies |
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6.3. Data Acquisition Techniques |
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6.4. Considerations for Representative and Diverse Corpora |
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6.5. Corpus Annotation |
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6.6. Applications of Annotated Corpora |
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6.7. Best Practices in Building Linguistics Corpora |
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Corpus Linguistics Lecture Notes |
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| Chapter 7 |
7.1. Introduction |
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7.2. Core Objectives |
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7.3. Lexical Databases |
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7.4. Expanding Lexical Resources |
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7.5. Automatic Extraction of Lexical Information |
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7.6. Challenges in Computational Lexicography |
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7.7. Applications of Computational Lexicography |
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Computational Lexicography Lecture Notes |
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| Chapter 8 |
Introduction |
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8.1. Language Teaching |
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8.2. Search Engines and Information Retrieval |
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8.3. Machine Translation |
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8.4. Sentiment Analysis |
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8.5. Speech Recognition Systems |
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| Chapter 9 |
Introduction |
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9.1. Applications in Healthcare |
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9.2. Applications in Education |
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9.3. Applications in Entertainment and Media |
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9.4. Ethical and Responsible Language Technology |
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| Resource Center |
Meet Alpha, your class tutor |
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 Tools for Research |
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Past Recordings |
Danh gia LATS Nguyen The Luong https://cloud05.ulearning.vn/playback/presentation/2.3/d4501acd3cda5ff4c87b07008a8de6f7c7cfc6c0-1637738196339
Webinar cua Nguyen Thi Hong Lien cho HVCT: https://cloud05.ulearning.vn/playback/presentation/2.3/eb677b81657eec7b58f1a7ba07e3b5f53bb02689-1642591431195
Vu tap huan cho LAC 7/4/2024: https://cloud05.ulearning.vn/playback/presentation/2.3/aaf9d545548cd1a20da0be92751cdbb8a51af2cd-1720053245124
Lien tap huan cho HVCT 17/1/2022: https://cloud05.ulearning.vn/playback/presentation/2.3/eb677b81657eec7b58f1a7ba07e3b5f53bb02689-1642418216035
Vu tap huan cho HVCT 21/1/2022: https://cloud05.ulearning.vn/playback/presentation/2.3/eb677b81657eec7b58f1a7ba07e3b5f53bb02689-1641986136908
Vu tap huan cho HVCT 14/1/2022: https://cloud05.ulearning.vn/playback/presentation/2.3/eb677b81657eec7b58f1a7ba07e3b5f53bb02689-1642159249548
Vu tap huan cho HVCT 21/1/2022: https://cloud05.ulearning.vn/playback/presentation/2.3/eb677b81657eec7b58f1a7ba07e3b5f53bb02689-1642764009388
Vu tap huan ki nang to chuc hoc va thi truc tuyen: https://cloud05.ulearning.vn/playback/presentation/2.3/2164641e1e2f28b0de3fc7f470774025a7ff3271-1678417085438
eb677b81657eec7b58f1a7ba07e3b5f53bb02689-1642159249548
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End of course feedback |
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History of Neural Networks |
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300 possible dimensions in Word Embeddings |
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TARI AI Tools Survey |
Please use TARI AI tools at https://tari.huflit.edu.vn before taking this survey. |
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Course WhiteBoard |
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All databases used in the book |
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