Abstract In this introductory paper to the special issue on “Weak cesuras in talk-in-interaction”, we aim guide reader into current work “chunking” of naturally occurring talk. It is conducted methodological frameworks Conversation Analysis and Interactional Linguistics – two approaches that consider interactional aspect humans talking with each other be a crucial starting point for its analysi...

Several phrase chunkers have been proposed over the past few years. Some state-of-the-art chunkers achieved better performance via integrating external resources, e.g., parsers and additional training data, or combining multiple learners. However, in many languages and domains, such external materials are not easily available and the combination of multiple learners will increase the cost of tr...

Citation: Gilchrist AL (2015) How should we measure chunks? a continuing issue in chunking research and a way forward. Generally defined, chunking is a process through which one reorganizes or groups presented information to compress information; it is one of the best-known methods of increasing the amount of information stored in memory. Chunking can occur by two different means: either throug...

We created a simple gold standard for English-Hungarian NP-level alignment, Orwell’s 1984, (since this already exists in manually verified POS-tagged format in many languages thanks to the Multex and MultexEast project) by manually verifying the automaticaly generated NP chunking (we used the yamcha, mallet and hunchunk taggers) and manually aligning the maximal NPs and PPs. The maximum NP chun...

In this paper, we describe an empirical study of Chinese chunking on a corpus, which is extracted from UPENN Chinese Treebank-4 (CTB4). First, we compare the performance of the state-of-the-art machine learning models. Then we propose two approaches in order to improve the performance of Chinese chunking. 1) We propose an approach to resolve the special problems of Chinese chunking. This approa...

Phrase pattern recognition (phrase chunking) refers to automatic approaches for identifying predefined phrase structures in a stream of text. Support vector machines (SVMs)-based methods had shown excellent performance in many sequential text pattern recognition tasks such as protein name finding, and noun phrase (NP)-chunking. Even though they yield very accurate results, they are not efficien...

In past work, chunking in Soar has been analyzed as a variant of explanation-based learning. The components and processes underlying EBL have been mapped to their corresponding components and processes in chunking. The cost and generality of the resulting rules have also been compared. Here we extend that work by analyzing an implementation of EBL within Soar as a sequence of transformations fr...