Coherence in Natural Language: Data Structures and Applications
A discussion of coherence in natural language that develops criteria for descriptively adequate data structures and examines the influence of coherence on psycholinguistic processes and determining the relative importance of document segments.
In Coherence and Natural Language, Florian Wolf and Edward Gibson specify and evaluate criteria for descriptively adequate data structures for representing discourse coherence. They test the influence of discourse structure on pronoun processing, evaluate different approaches for determining the relative importance of document segments, and propose a new coherence-based algorithm to accomplish this task. Their book offers the first large-scale empirical evaluation of data structures for representing coherence and also includes novel psycholinguistic tests of existing information extraction and text summarization systems. Wolf and Gibson evaluate whether tree structures are descriptively adequate for representing discourse coherence and conclude that more powerful data structure is needed because there are many different kinds of crossed dependencies and nodes with multiple parents in the discourse structures of naturally occurring texts. They propose that connected, labeled chain graphs make a better representation of coherence. They find additionally that causal coherence relations affect people's strategies for pronoun processing, which points to the psychological validity of coherence relations. Finally, they evaluate word-based, layout-based, and coherence-based approaches for estimating the importance of document segments in a document and find that coherence-based methods that operate on chain graphs perform best. With its attention to empirical validation and psycholinguistic processing, the book raises issues that are relevant to cognitive science as well as natural language processing and information extraction.