Discourse Topics, Linguistics, and Language Teaching

Richard Watson ToddKing Mongkut’s University of Technology Thonburi

arts.kmutt.ac.th/crs/research/

Discourse_Topics_Linguistics_and_Language_Teaching

Topic

• Everyday word– Changing the topic– The main topic of conversation– How to broach the topic– Go off topic– The hot topic

Topic

Topics in applied linguistics

• Reading comprehension

• The language of the cognitively impaired

• Natural language processing

• Analysis of texts and conversations

Topics and reading comprehension

• How can topics affect reading comprehension?

• Wrestling

• The prison break

• Rocky slowly got up from the mat, planning his escape. He hesitated a moment and thought. Things were not going well. What bothered him most was being held, especially since the charges against him had been weak. He considered his present situation. The lock that held him was strong but he thought he could break it. He knew, however, that his timing would have to be perfect.

The language of the cognitively impaired

• Topic maintenance patterns and signalling of topic changes differs from normal practice in– People with severe schizophrenia– People with Alzheimer’s disease– Certain types of head injuries– Certain types of aphasia

Natural Language Processing

• Automated topic identification and topic boundary identification

• Applications for text summarisation, automated translation, automated essay scoring

• Sample programs/approaches: Text Tiling, Texplore, TopCat

Analysis of discourse

• Text linguistics– Topicality as the general organising principle in

discourse

• Discourse processing– Relevance theory (and hence topics) as underpinning

discourse

• Conversation analysis– Clumping by topic as a key explanation of

conversation structure

• Language features in discourse– Relationship between prosody and topic

Problems with topic

• Much previous work in linguistics involving topics has– Not clearly identified what topic means– Relied on intuitive identifications of topic

(especially DA and CA)– Focused on where topics change but not what

topics are (especially DA and NLP)– Made problematic assumptions about

communication e.g. its logicality (especially NLP)

Topic issues: Defining topic

• Replacement definitions– The topic is the “subject”

• Area-specific definitions– “information which has a low degree of

recoverability and which has persistence”

• More general definitions– “cluster of associated or related concepts”

Topic issues: Stating topics

• 3 choices:– Topic as entity (i.e. noun phrase)– Topic as proposition (i.e. sentence)– Topic as question (which the relevant

discourse answers)

Topic issues: Level of topic

• Topics identified by text length– Sentence topic (in topic-comment approach

similar to theme-rheme analysis)– Paragraph topic (similar to main idea)– Text topic

• Topics identified by text features– Topic of stretch of discourse between 2 topic

boundaries

Topic issues: Topic boundaries

• Identifying topic boundaries– By prosody– By discourse markers (e.g. temporal

adverbials; genre-specific markers e.g. “This just in”)

– By pronoun referent shift– By change in content

Topic issues: Topic change

• Types of topic progression– Topic introduction– Topic maintenance– Topic shift– Topic drift/shading– Topic renewal– Topic insertion

Topic issues: Topic identification

• Topics identified in text (especially NLP)

• Topics identified in interaction between reader and text

• Topics identified through participant behaviour (especially CA)

• General assumption that stretches of discourse have a unique topic

Topic issues: Methods of analysis

• Word frequency• Phrase frequency• Word co-occurrence frequency• Text position analysis• Lexical cohesion analysis• Theme-rheme progression• Given-new progression• Topic-based analysis (using logical relations or

associations)

Assumptions of methods of analysis

• Word frequency/word co-occurrence frequency/lexical cohesion– Concepts are expressed by single words– Frequency is indicator of importance

• Theme-rheme progression– Sentence topics combine to form discourse topics

• Topic-based analysis (using logical relations)– Discourse structure and cognition are both

hierarchical

Gaps in topic

• Testing validity of methods of analysis

• Questioning assumptions underlying topic research

• Investigating participant identification of topic

Filling the gaps: Study 1

• Testing validity of methods of analysis• Using extracts from teacher eliciting from 12

lessons• Apply 6 methods to the data to identify topics

and topic boundaries (and a control method)– Theme-rheme progression– Given-new progression– Lexical cohesion analysis– Topic-based analysis (using logical relations)– Topic-based analysis (using associations)– Exchange structure analysis

Comparing methods for identifying topic boundaries

Comparing methods for identifying topics

Implications from comparisons

• Exchange structure analysis (based on functions) and theme-rheme progression (based on surface forms) are problematic

• Other approaches are based on discourse semantics or cognition

• Network-based analyses cluster together

Filling the gaps: Study 2

• Questioning assumptions underlying topic research

• Same data as in Study 1; same 6 approaches but with bias based on findings from Study 1

• Combine findings from the approaches

Combining findingsFigure 3: Fuzzy topics in an extract of classroom discourse

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Implications from combining findings

• There is no single definitive topic for any stretch of discourse

• At any point, several concepts have the potential to be considered as (part of) the topic at different levels of likelihood

Filling the gaps: Study 3

• Investigating participant identification of topics• The data is an extract from An Inconvenient

Truth• Topics and topic boundaries are identified by 7

informants• Topics and topic boundaries are analysed using

4 methods• Findings from informants and methods are

compared

Implications from Study 3

• Reasonable agreement in identifying topic boundaries and topics– Between informants– Between methods of analysis– Between informants and methods

• For topic boundaries, given-new progression matches informants most closely

• For topics, topic-based analysis (using logical relations) matches informants most closely

Implications from Study 3

• Topic keywords = words included as part of the noun phrase describing the topic

• All topic keywords identified by the 4 methods appear in the text

• 28% of topic keywords identified by informants do not appear in the text (e.g. words showing rhetorical function)

• Correlation between topic keywords and word frequency in the text is 0.65

What does all this mean?

• For discourse analysts and neuropsychologists– Most topic identification relies on researcher

intuition– Ask participants about topics– Conduct loose analyses (using given-new

progression and topic-based analysis)– Be open to multiple topics– Avoid theme-rheme progression

What does all this mean?

• For Natural Language Processing– Most NLP applications are very reliant on

frequency– Attempt to account for saliency and rhetorical

functions– Consider identifying multiple topics (fits with

Bayesian approach)

What does all this mean?

• For language teachers– Topics most central in teaching reading– Reading for the main idea → Reading for a

main idea?– If multiple topics exist, avoid correct/incorrect

approaches to reading– Multiple topics fit with a constructivist

approach

Summary

• Topics are central in much of linguistics

• Most informal use of topics in linguistics is problematic

• More rigorous approaches to topic are needed

• Need for more work on topic as a linguistic concept