Broad coverage paragraph segmentation across languages and domains

Automatic Paragraph Identification: A Study across Languages and Domains

In this paper we investigate whether paragraphs can be identified automatically in different languages and domains. We propose a machine learning approach which exploits textual and discourse cues and we assess how well humans perform on this task. Our best models achieve an accuracy that is significantly higher than the best baseline and, for most data sets, comes to within 6% of human perform...

Metacognitive (Rending) Strategies Across Languages and Techniques

Comprehension across Application Domains and Languages

This work demonstrates that our natural language understanding framework can be applied across application domains and languages with ease. Approaches towards language understanding generally involve much handcrafting, e.g. in writing grammars or annotating corpora, hence portability is a desirable trait in the development of language understanding systems. Our framework for natural language un...

Multi - Paragraph Segmentation of ExpositoryTextsMarti

We present a method for partitioning expository texts into coherent multi-paragraph units which reeect the subtopic structure of the texts. Using Chafe's Flow Model of discourse, we observe that subtopics are often expressed by the interaction of multiple simultaneous themes. We describe two fully-implemented algorithms that use only term repetition information to determine the extents of the s...

Broad Coverage Automatic Morphological Segmentation of German Words

A system for the automatic segmentation of German words into morphs was developed. The main linguistic knowledge sources used by the system are a word syntax and a morph dictionary. The syntax is written in the formalism of right linear regular grammars and comprises approximately 1,400 rules describing the set of those sequences of morph classes which underlie syntactically well formed words. ...