It is established that temporal grouping or “chunking” arises in serial recall as it does in speech. For instance, chunking appears in common tasks like remembering series such as phone numbers. In the present study, we examine how detected chunks in meaningless strings of syllables and meaningful utterances influence memory. We use a Sternberg task where listeners identify whether a heard item...

Indexing large bodies of data is necessary to enable satisfactory search results. Ontologies serve as fixed vocabularies to index data from different viewpoints. We describe how AIDAS, a software tool, automatically divides the source data (PDF documents) into reusable chunks, how it automatically indexes these chunks and stores them in a database to enable reuse.

This paper is about syntactic analysis of natural language sentences. The focus is on wide coverage partial parsing architectures. In this work we enhance and enrich the UCSG shallow parsing architecture being developed here over the last many years. UCSG architecture combines linguistic grammars in the form of Finite State Machines for recognising all potential chunks and HMMs to rate and rank...

As these approaches are confined to representing event structural properties, the idiosyncratic lexical content is often reduced to an unanalyzed atomic root. In our talk we will demonstrate by the example of verbs of emission that a more fine-grained analysis is necessary in order to account for the semantics of these verbs. Following the traditional approach in Rappaport Hovav and Levin (2010...

The motivation of this project is to solve a problem that we currently face working on a project called 27bards, which seeks to revolutionize the tour industry by constructing a database of currently existing tour operators with the Airbnb-like twist of allowing the average person to make money as a part-time tour guide. We wanted to apply machine-learning techniques to make sense of the largel...

How students learn to use diagrammatic representations is an important topic in the design of effective representations for problem solving or conceptual learning, but few good models of their learning exist. In this paper, we explore the learning process with an experiment using AVOW diagrams as a representation for solving problems in electric circuits. We find that the participants decompose...

In this paper, the problem of designing network codes that are both communicationally and computationally efficient over packet line networks with worst-case schedules is considered. In this context, random linear network codes (dense codes) are asymptotically capacity-achieving, but require highly complex coding operations. To reduce the coding complexity, Maymounkov et al. proposed chunked co...

Frequent itemset mining is extensively employed as an essential data technique. Nevertheless, the size grows, applicability of this method decreases owing to relatively poor performance existing methods. Though numerous efficient sequential frequent methods have been developed, that can be achieved clearly limited by fact they exploit only one thread. To overcome these limitations, a number par...

This paper explores the benefits and challenges of using the ACT-R cognitive architecture in the development of a largescale, functional, cognitively motivated language analysis model. The paper focuses on ACT-R’s declarative memory retrieval mechanism, proposing extensions to support verification of retrieved chunks, multi-level activation spread and carry over activation. The paper argues aga...