Linguistics → Computational Linguistics → Discourse Analysis

Description:

Discourse Analysis in the context of Computational Linguistics is a field focused on understanding and interpreting language use beyond the sentence level – it examines how sequences of sentences, or “discourses,” form coherent and meaningful interactions. This area of study explores not just what is said, but how it is said, to whom, and for what purpose. Unlike traditional syntactic and semantic analysis, discourse analysis considers the broader context, including social, cultural, and situational factors that influence communication.

Key Concepts:

1. Cohesion and Coherence
   • Cohesion refers to the grammatical and lexical linking within a text or sentence that holds a text together and gives it meaning. Tools of cohesion include conjunctions, pronouns, and lexical repetition.
   • Coherence is a broader concept that includes the cognitive connections and logical relationships among sentences and larger discourse units, ensuring the text makes sense as a whole.
2. Discourse Structure
   • This pertains to how different parts of the discourse are organized and related. Common structures include narrative, description, argument, and exposition.
   • Computational models may represent discourse structures using graphs or trees, where nodes represent discourse units and edges represent the relationships between them.
3. Speech Acts and Pragmatics
   • Speech act theory examines how utterances function not just to convey information but to perform actions (e.g., requests, promises, assertions).
   • Pragmatics involves understanding the intended meaning behind utterances considering factors like speaker intent, context, and implications.
4. Reference Resolution
   • This involves identifying the entities (e.g., people, objects) to which pronouns and other referring expressions point. Algorithms for reference resolution analyze context and prior discourse to determine these referents accurately.
5. Topic Modeling and Segmentation
   • Topics are the subjects or themes around which discourse is centered. Topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), can identify and track topics within a text corpus.
   • Segmenting discourse into coherent units (e.g., paragraphs, conversation turns, sections) is essential for analysis and understanding.

Computational Approaches:

1. Natural Language Processing (NLP) Techniques
   • NLP tools like tokenization, part-of-speech tagging, named entity recognition, and parsing lay the groundwork for discourse analysis by breaking down and categorizing text elements.
   • Machine learning models, especially those based on deep learning like transformers (e.g., BERT, GPT), are employed to understand and predict discourse-level phenomena.
2. Corpus Linguistics
   • A corpus-based approach involves analyzing large collections of texts to identify patterns and structures in discourse. Annotated corpora are often used to train and test computational models of discourse.
3. Statistical and Probabilistic Models
   • These models, including Hidden Markov Models (HMM) and Conditional Random Fields (CRF), can be used to predict sequential discourse elements.
   • Bayesian approaches may help in understanding the probabilistic relationships between discourse units.

Applications:

1. Automatic Summarization
   • Understanding discourse helps in extracting salient points from texts to generate concise summaries.
2. Sentiment Analysis
   • Examining discourse allows for more accurate detection of sentiments, emotions, and opinions expressed across longer text spans.
3. Conversational Agents
   • Discourse analysis improves the naturalness and coherence of interactions with virtual assistants and chatbots.
4. Information Retrieval
   • Enhanced context awareness in search engines aids in retrieving more relevant documents by understanding the discourse context of queries.

By combining insights from linguistics, cognitive science, and computer science, computational discourse analysis seeks to model, interpret, and generate text in ways that are nuanced, contextually aware, and aligned with human communication practices. This interdisciplinary field continues to evolve, driven by advancements in computational techniques and a deeper understanding of language use.