This paper presents a new Chinese chunking method based on maximum entropy Markov models. We firstly present two types of Chinese chunking specifications and data sets, based on which the chunking models are applied. Then we describe the hidden Markov chunking model and maximum entropy chunking model. Based on our analysis of the two models, we propose a maximum entropy Markov chunking model th...

Spec is a critical issue for automatic chunking. This paper proposes a solution of Chinese chunking with another type of spec, which is not derived from a complete syntactic tree but only based on the un-bracketed, POS tagged corpus. With this spec, a chunked data is built and HMM is used to build the chunker. TBLbased error correction is used to further improve chunking performance. The averag...

This paper describes the tests made on chunking methods used for plagiarism detection. The result of the tests makes it possible to decide on the best fitting chunking method for a given application. For example, overlapping word chunking is good for a grammar analyzer or for small databases, sentence chunking suits best for finding quoted texts, hashed breakpoint chunking is the fastest method...

After elaborating the definition of working memory, the relationship between short-term memory and working memory, chunking in SLA and the relationship between short-term memory and chunking, this paper proves the importance of chunking through the experiment: the students’ capacity in fast reading, reading in depth, listening and cloze from experimental group was affected by vocabulary depth t...

Soar is an architecture for intelligence that integrates learning into all of its problem-solving behavior. The learning mechanism, chunking, has been studied experimentally in a broad range of tasks and situations. This paper summarizes the research on chunking in Soar, covering the effects of chunking in different tasks, task-independent applications of chunking and our theoretical analyses o...

Entropy Guided Transformation Learning (ETL) is a new machine learning strategy that combines the advantages of decision trees (DT) and Transformation Based Learning (TBL). In this work, we apply the ETL framework to four phrase chunking tasks: Portuguese noun phrase chunking, English base noun phrase chunking, English text chunking and Hindi text chunking. In all four tasks, ETL shows better r...

Virtual education has allowed for the formation of new forms teaching during synchronous classes, thereby replacing traditional expository teaching. The aim this study was to analyse influence chunking classes on intrinsic motivation university students enrolled in different academic programs a public Peru. A total 114 and three professors participated study. were systematised consider beginnin...

Several recent empirical findings have reinforced the notion that a basic learning and memory skill—chunking—plays a fundamental role in language processing. Here, we provide evidence that chunking shapes sentence processing at multiple levels of linguistic abstraction, consistent with a recent theoretical proposal by Christiansen and Chater (2016). Individual differences in chunking ability at...

The chunking hypothesis suggests that during the repeated exposure of stimulus material, information is organized into increasingly larger chunks. Many researchers have not considered the full power of the chunking hypothesis as both a learning mechanism and as an explanation of human behavior. Indeed, in developmental psychology there is relatively little mention of chunking and yet it can be ...