LIN 309 — Computational Linguistics I: Lecture Note

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LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS

LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS Course code: LIN 309 Course title: Computational Linguistics I Course credit load: 2 Units Series 1: Foundations of Computational Linguistics Series 2: Natural Language Processing Problems and Perspectives Series 3: Corpus Linguistics Essentials Series 4: Probability Calculus in Language Analysis Series 5: N-Grams and Language Modeling Proposed Outline for Series 1 Part .1 Concepts and Scope of Computational Linguistics • 1.1.1 Definition and nature of computational linguistics • 1.1.2 Historical development and relevance to language studies • 1.1.3 Key applications in modern contexts Part .2 Core Terminologies and Principles • 1.2.1 Essential vocabulary in computational linguistics • 1.2.2 Principles guiding computational approaches to language • 1.2.3 Relationship between linguistics and computer science Part .3 Computational Linguistics as a Discipline • 1.3.1 Interdisciplinary connections (linguistics, computer science, statistics) • 1.3.2 Role in natural language processing (NLP) • 1.3.3 Emerging trends and perspectives Part .4 Practical Orientation and Learning Pathways • 1.4.1 Skills required for computational linguistics • 1.4.2 Typical problems addressed in the field • 1.4.3 Preparing for advanced topics in subsequent Series Introduction

LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS Language is one of the most powerful tools humans possess, yet making computers process and interpret it requires a special field of study known as computational linguistics. This Series sets the foundation for your journey into the discipline, helping you to appreciate how linguistics and computer science come together to solve language-related problems. By engaging with this Series, you will be well prepared to tackle the more advanced topics that follow in the course. The aim of this Series is to introduce you to the fundamental concepts, terminologies, and guiding principles of computational linguistics, and to equip you with the ability to explain its scope, relevance, and interdisciplinary nature. We shall commence this Series by exploring the concepts and scope of computational linguistics, including its definition, nature, and historical development. Next, we will move on to examine the core terminologies and principles that underpin the field. Following that, we will consider computational linguistics as a discipline, highlighting its interdisciplinary connections and role in natural language processing. Finally, we will wrap up by discussing the practical orientation of the field, focusing on the skills required, typical problems addressed, and pathways for further study. Series Learning Outcomes Upon completing this Series, you should be able to: • SLO 1.1 define the concept and scope of computational linguistics, • SLO 1.2 explain the historical development and relevance of computational linguistics to language studies, • SLO 1.3 identify and describe core terminologies and guiding principles in computational linguistics, • SLO 1.4 analyse the interdisciplinary connections of computational linguistics with linguistics, computer science, and statistics, • SLO 1.5 evaluate the role of computational linguistics in natural language processing, and • SLO 1.6 demonstrate awareness of practical skills, typical problems, and pathways for further study in computational linguistics. 1.1 Concepts and Scope of Computational Linguistics 1.1.1 Definition and nature of computational linguistics Computational linguistics is the scientific study of language using computational methods. It involves applying algorithms, models, and computer systems to analyse, process, and generate human language. This field bridges the gap between linguistics, which studies the structure and use of language, and computer science, which provides the tools and techniques to handle large-scale language data.

LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS Computational linguistics is not limited to programming or linguistics alone. It is an interdisciplinary domain that combines insights from mathematics, statistics, psychology, and artificial intelligence. Learners should note that computational linguistics is both theoretical, in its attempt to model language, and practical, in its application to real-world problems such as machine translation and speech recognition. Fill in the blank: Computational linguistics is the scientific study of language using __________ methods. Figure 1.1 Relationship between linguistics, computer science, and computational linguistics 1.1.2 Historical development and relevance to language studies The origins of computational linguistics can be traced back to the mid-twentieth century when researchers began exploring how computers could process human language. Early efforts focused on machine translation, which is the automatic conversion of text from one language to another. Although these early systems were limited, they laid the foundation for modern natural language processing. Over time, computational linguistics expanded to include speech recognition, text analysis, and dialogue systems. Today, it plays a crucial role in everyday technologies such as search engines, digital assistants, and grammar checkers. Its relevance to language studies lies in its ability to provide empirical methods for analysing large corpora of text, thereby offering insights that traditional linguistic approaches may not easily uncover. Fill in the blank: The earliest application of computational linguistics was in the field of __________ translation.

LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS Plate 1.1 Early computer systems for language processing 1.1.3 Key applications in modern contexts Modern computational linguistics has a wide range of applications. For example, speech recognition refers to the ability of computers to convert spoken language into text. Text-to-speech synthesis allows computers to generate spoken language from written text. Information retrieval enables search engines to locate relevant documents quickly, while sentiment analysis helps businesses evaluate customer opinions expressed in text. These applications demonstrate how computational linguistics impacts daily life, from using mobile phones to interacting with intelligent assistants. Learners should recognise that the scope of computational linguistics is not confined to academic research but extends to practical tools that shape communication and information access. Fill in the blank: Sentiment analysis is used to evaluate __________ expressed in text. Application Description Real-World Example Speech Recognition Converting spoken language into text by identifying phonetic patterns and linguistic context. Voice assistants like Siri or Alexa; automated captioning. Text-to-Speech (TTS) Synthesizing artificial human speech from written text using prosody and intonation rules. Screen readers for the visually impaired; GPS navigation voices. Machine Translation Automatically translating text or speech from one language to another using neural networks. Google Translate; real-time translation during video calls. Sentiment Analysis Identifying and extracting subjective information (emotions, opinions) from text. Analyzing Amazon product reviews or social media feedback for brand sentiment.

LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS Application Description Real-World Example Information Retrieval Modeling how to find and extract relevant information from large-scale databases or the web. Search engines like Google; academic database indexing. Box 1.1 Selected applications of computational linguistics Pilot questions 1.1 1. Define computational linguistics in your own words. 2. Identify two disciplines that contribute to computational linguistics. 3. What was the earliest application of computational linguistics? 4. Mention two modern applications of computational linguistics. 5. Explain why computational linguistics is relevant to language studies. 1.2 Core Terminologies and Principles 1.2.1 Essential vocabulary in computational linguistics Terminology is central to mastering computational linguistics. Some of the most important terms include: • Algorithm: A step-by-step procedure for solving a problem or performing a task. • Corpus: A large collection of texts used for linguistic analysis. • Parsing: The process of analysing the grammatical structure of a sentence. • Tokenisation: The division of text into smaller units such as words or sentences. These terms will recur throughout the course, so it is important to be comfortable with them. Fill in the blank: The process of dividing text into smaller units such as words or sentences is called __________. Term Definition Role in the Process Corpus A large, structured collection of machine-readable texts used for linguistic analysis. Provides the raw data and real-world evidence for training models. Tokenization The process of breaking down a stream of text into smaller units, such as words or phrases (tokens). The first step in processing; it turns a sentence into a list of individual items.

LA-NET | ONLINE LEARNING VIDEO PLATFORM FOR POST-SECONDARY & HIGHER INSTITUTION STUDENTS Term Definition Role in the Process Parsing Analyzing a string of tokens to determine its grammatical structure (e.g., identifying subjects and verbs). Assigns syntactic meaning; it helps the computer understand "who did what to whom." Algorithm A set of defined rules or instructions used by a computer to solve a specific problem or perform a task. The engine that drives the analysis, from simple sorting to complex neural network predictions. Box 1.2 Selected computational linguistics terms 1.2.2 Principles guiding computational approaches to language Computational linguistics is guided by several principles. One principle is formalisation, which means representing language phenomena in precise, rule-based or mathematical terms. Another is empiricism, which stresses the importance of using real-world data to test theories. A third principle is automation, which involves designing systems that can process language with minimal human intervention. These principles ensure that computational linguistics remains both scientific and practical. Fill in the blank: The principle that stresses the importance of using real-world data to test theories is called __________. 1.2.3 Relationship between linguistics and computer science Computational linguistics exists at the intersection of linguistics and

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