1.2 Linguistics basics for NLP

Natural Language Processing (NLP) relies heavily on linguistic basics. Understanding morphology, syntax, and semantics is crucial for developing effective NLP systems that can accurately process and interpret human language.

These linguistic principles form the foundation for various NLP tasks. From tokenization and part-of-speech tagging to parsing and semantic analysis, linguistic knowledge enhances the accuracy and efficiency of NLP applications across the board.

Fundamental Concepts of Language

Morphology: Studying Word Structure and Formation

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• Examines the internal structure of words and the rules governing word formation from morphemes, the smallest meaningful units in a language (prefixes, suffixes)
• Classifies morphemes as free morphemes that can stand alone as words (cat, run) or bound morphemes that must attach to other morphemes to form words (un-, -ing)
• Analyzes processes like inflection that modifies word forms to express grammatical categories (plural nouns, past tense verbs) and derivation that creates new words from existing ones (happy → happiness)
• Contributes to NLP tasks such as stemming that reduces words to their base form (running → run) and lemmatization that determines the dictionary form of words (better → good)

Syntax: Analyzing Sentence Structure and Relationships

• Focuses on the rules and principles governing the structure of sentences in a language, including the arrangement of words, phrases, and clauses
• Represents syntactic structures using parse trees that show the hierarchical relationships between sentence elements (noun phrases, verb phrases) or dependency graphs that depict word-to-word connections
• Examines concepts like constituency, the idea that words combine to form larger units (the black cat → noun phrase), and recursion, the ability to embed phrases within others (the cat that chased the mouse → recursive noun phrase)
• Applies syntactic knowledge to NLP tasks such as grammar checking, sentiment analysis that determines the emotional tone of a text, and information extraction that identifies key entities and relationships in a document

Semantics: Interpreting Meaning in Language

• Studies the meaning of words, phrases, and sentences in context, going beyond the literal interpretation to consider factors like ambiguity, implicature, and presupposition
• Explores semantic relationships between words, including synonymy (couch, sofa), antonymy (hot, cold), hyponymy (rose → flower), and meronymy (petal → flower)
• Applies compositional semantics to determine the meaning of a sentence based on the meanings of its constituent words and their syntactic arrangement (The old man walked slowly. → The man, who is old, walked in a slow manner.)
• Contributes to NLP tasks such as word sense disambiguation that identifies the intended meaning of a word in context (river bank vs. financial bank), named entity recognition that classifies named entities into predefined categories (person, location), and question answering that provides accurate responses to user queries

Linguistic Knowledge for NLP

Importance of Linguistic Knowledge in NLP Systems

• Enables accurate and efficient processing, understanding, and generation of human language by incorporating insights from morphology, syntax, and semantics
• Improves text normalization and feature extraction through morphological analysis techniques like stemming that reduces words to their base form (running → run) and part-of-speech tagging that assigns grammatical categories to words (noun, verb)
• Enhances tasks like grammar checking, sentiment analysis, and information extraction by leveraging syntactic parsing to understand the structure and relationships between words in a sentence
• Facilitates interpretation of meaning in context for tasks such as word sense disambiguation, named entity recognition, and question answering by applying semantic analysis
• Reduces ambiguity and generates more grammatically correct and semantically coherent outputs by incorporating linguistic rules and constraints into NLP models

Integration of Linguistic Knowledge in NLP Tasks

• Tokenization: Utilizes morphological knowledge to develop accurate tokenizers that can handle complex words (hyphenated compounds), contractions (can't → can not), and multi-word expressions (New York)
• Part-of-speech tagging: Applies syntactic rules and constraints to improve tagger accuracy, especially for ambiguous (duck as noun or verb) or out-of-vocabulary words
• Parsing: Employs syntactic theories and algorithms to build robust parsers that can handle diverse sentence structures and deal with ambiguity (prepositional phrase attachment) and long-distance dependencies
• Word sense disambiguation: Leverages semantic knowledge and context to identify the intended meaning of words in different contexts (bank as financial institution or river edge)
• Machine translation: Incorporates linguistic principles to ensure translated text maintains the grammatical structure, semantic meaning, and idiomatic expressions of the target language
• Text generation: Uses linguistic rules and constraints to generate grammatically correct, semantically coherent, and stylistically appropriate text for applications like chatbots, summarization, and creative writing

Structure and Properties of Language

Hierarchical and Systematic Nature of Language

• Exhibits a hierarchical structure where smaller units (phonemes, morphemes) combine to form larger units (words, phrases, sentences) according to specific rules and constraints
• Demonstrates productivity by allowing the creation of an infinite number of novel utterances using a finite set of elements and rules
• Follows regular patterns and structures that can be described using formal grammars and linguistic theories, making language systematic and analyzable

Arbitrariness and Context-Dependence of Language

• Displays arbitrariness, with no inherent connection between the form of a word and its meaning, except in limited cases like onomatopoeia (buzz, hiss) and sound symbolism (gl- in glitter, glimmer)
• Relies on the surrounding linguistic and extralinguistic context for the interpretation of words and sentences, making language context-dependent
• Requires consideration of factors like speaker's intention, shared knowledge, and social setting to accurately interpret meaning in communication

Language Evolution and Variation

• Constantly evolves over time due to various social, cultural, and historical factors, leading to changes in vocabulary (neologisms), pronunciation (sound shifts), and grammar (syntactic changes)
• Exhibits variation across different geographical regions (dialects), social groups (sociolects), and individual speakers (idiolects)
• Adapts to the needs and preferences of language users, reflecting changes in society, technology, and cultural values

Linguistic Principles in NLP

Application of Morphological Principles

• Develops accurate and efficient tokenizers that can handle complex words (compound nouns), contractions (I'm → I am), and multi-word expressions (United States) by utilizing knowledge of morphological structure and rules
• Improves the performance of stemming algorithms that reduce words to their base form (running → run) and lemmatization techniques that determine the dictionary form of words (better → good) by considering morphological patterns and irregularities
• Enhances part-of-speech tagging by leveraging information about word formation processes (affixation) and morphological features (plural, past tense) to assign accurate grammatical categories to words

Integration of Syntactic Knowledge

• Builds robust parsers that can handle a wide range of sentence structures by employing syntactic theories (dependency grammar, phrase-structure grammar) and algorithms (CYK, Earley)
• Resolves ambiguity in parsing, such as prepositional phrase attachment (The man saw the girl with the telescope. → attachment to "saw" or "girl"?), by applying syntactic constraints and heuristics
• Improves the accuracy of tasks like grammar checking, sentiment analysis, and information extraction by leveraging syntactic information to identify grammatical errors, determine the scope of sentiment-bearing words, and extract relevant entities and relationships

Application of Semantic Principles

• Develops models for word sense disambiguation that accurately identify the intended meaning of words in different contexts (bank as financial institution or river edge) by leveraging semantic knowledge and contextual cues
• Improves named entity recognition by using semantic information to classify named entities into predefined categories (person, location, organization) and resolve ambiguities (Washington as a person or location)
• Enhances question answering systems by applying semantic analysis to understand the intent behind user queries, identify relevant information in a knowledge base, and generate accurate and coherent responses
• Generates grammatically correct, semantically coherent, and stylistically appropriate text for various applications (chatbots, summarization, creative writing) by incorporating semantic constraints and rules to ensure meaningful and contextually relevant output