Journal

Volume 12, Issue 3 (September 30, 2011)

6 articles

• Introduction to this special issue on Cognitive Aspects of Natural Language Processing
  by Michael Zock & Reinhard Rapp
  J. CS. 2011, 12(3), 211-213;
  Abstract (No Abstract)1st paragraph:Realising that no single discipline will ever suffice to reveal the functioning of the human mind, cognitive science was born. The aim from the outset was to analyse complex behaviour (speaking, thinking, problem-solving) from different angles and to build models that coul... [Read more].
  Abstract (No Abstract)1st paragraph:Realising that no single discipline will ever suffice to reveal the functioning of the human mind, cognitive science was born. The aim from the outset was to analyse complex behaviour (speaking, thinking, problem-solving) from different angles and to build models that could account for this complexity. [Collapse]
• A Proposal for Building the Knowledge Base of Onomasiological Dictionaries
  by Gerardo Sierra & Laura Hernández
  J. CS. 2011, 12(3), 215-232;
  Abstract In this paper we present a methodology for creating and populating a lexical knowledge base (LKB) to be used in an onomasiological dictionary. The purpose of this methodology is to automatize the creation of specialized onomasiological dictionaries, which help to solve the “tip of the tongue” proble... [Read more].
  Abstract In this paper we present a methodology for creating and populating a lexical knowledge base (LKB) to be used in an onomasiological dictionary. The purpose of this methodology is to automatize the creation of specialized onomasiological dictionaries, which help to solve the “tip of the tongue” problem and assist authors in the active linguistic state (encoding). This article includes information about the main architecture of the LKB as well as our proposed dictionary. The methodology presented in this article allows the LKB to be populated with a wide variety of definitions from both colloquial and normative sources in such a way that by employing this LKB these specialized onomasiological dictionaries are able to handle users’ queries in natural languages. [Collapse]
• Storage does not Guarantee Access: The Problem of Organizing and Accessing Words in a Speaker's Lexicon
  by Michael Zock & Didier Schwab
  J. CS. 2011, 12(3), 233-258;
  Abstract Natural language production requires both a grammar and a lexicon. In this article, we deal only with the latter, trying to enhance an existing electronic resource to allow for search via navigation in a huge associative network. Our primary focus is on the structure of the lexicon (i.e. its indexin... [Read more].
  Abstract Natural language production requires both a grammar and a lexicon. In this article, we deal only with the latter, trying to enhance an existing electronic resource to allow for search via navigation in a huge associative network. Our primary focus is on the structure of the lexicon (i.e. its indexing scheme). This issue has often been overlooked, yet it is crucial, as it determines to a large extent the chances of finding the word a language user (speaker/writer) is looking for. While researchers working on natural language generation (NLG) have given a lot of thought to lexicalization (i.e. the mapping of meanings to forms), lexical access has received no attention at all. Lexicalization is generally considered to be only a choice problem, the assumption being that stored data can always be accessed. While this may hold for machines, it does not always hold for people, as is well attested by the “tip-of-the-tongue” problem. A speaker may know a word, yet still be unable to access it. However, even machines may experience access problems. We illustrate this last point via a small experiment, showing how a well-known lexical resource (WordNet) may fail to reveal information (words) it contains. Additionally, in this article we show how a lexicon might be organized or indexed to allow language users to find the words they are looking for quickly and naturally. [Collapse]
• An Associative Concept Dictionary for Natural Language Processing: Text Summarization and Word Sense Disambiguation
  by Jun Okamoto & Shun Ishizaki
  J. CS. 2011, 12(3), 259-276;
  Abstract We constructed an Associative Concept Dictionary based on the results from large-scale association experiments with participants in order to develop simulation models and systems of understanding natural language. In this paper, we first briefly explain the construction of this dictionary and descri... [Read more].
  Abstract We constructed an Associative Concept Dictionary based on the results from large-scale association experiments with participants in order to develop simulation models and systems of understanding natural language. In this paper, we first briefly explain the construction of this dictionary and describe its unique features; we then show how to apply it to text summarization and word sense disambiguation. The text summarization method uses this dictionary for calculating the importance scores of sentences. A Contextual Semantic Network that includes the semantic relations and quantitative distance information among words is constructed as a model of human contextual understanding using this dictionary. We compare the quality of the summarization with that of human participants and that using conventional methods such as term frequencies. Our method shows that the quality of summarization is better than that of conventional methods. The word sense disambiguation method uses a Dynamic Contextual Network Model constructed using this dictionary. An interactive activation method on the network is used in this system as a model for the human dynamic contextual understanding to identify a word’s meaning. The results show that such dynamic features are effective. [Collapse]
• Automatic Recognition of Emotion based on a Cognitively Motivated Emotion Annotation System
  by Ying Chen, Sophia Yat Mei Lee, & Chu-Ren Huang
  J. CS. 2011, 12(3), 277-296;
  Abstract Emotion recognition is very important for the extraction of expressive information. In this paper, we provide a robust and versatile emotion annotation scheme that can not only annotate explicit and implicit expressions of emotion, but also can encode different levels of information for a given emot... [Read more].
  Abstract Emotion recognition is very important for the extraction of expressive information. In this paper, we provide a robust and versatile emotion annotation scheme that can not only annotate explicit and implicit expressions of emotion, but also can encode different levels of information for a given emotion content. In addition, taking cognitive psychologists’ theories into account, large and comparatively high-quality emotion corpora are automatically created to allow for emotion recognition in Chinese and English. Our annotation scheme can easily be adapted for different kinds of applications dealing with emotion, and, being generic, can also be applied to other languages. We also discuss the two kinds of emotion representations used in our corpus, namely, holistic and componential representations. We find that the two representations have their own qualities and shortcomings and that choosing between them ultimately depends mainly on the application type. [Collapse]
• Language Acquisition as the Detection, Memorization, and Reproduction of Statistical Regularities in Perceived Language
  by Reinhard Rapp
  J. CS. 2011, 12(3), 297-322;
  Abstract In this article we investigate the hypothesis that language learning is based on the detection and memorization of particular statistical regularities as observed in perceived language, and that during language production these regularities are reproduced. We give an overview of those regularities w... [Read more].
  Abstract In this article we investigate the hypothesis that language learning is based on the detection and memorization of particular statistical regularities as observed in perceived language, and that during language production these regularities are reproduced. We give an overview of those regularities where we have been able to exemplify this behaviour. Our finding is that not only statistics of order zero (frequencies) and one (co-occurrences) are of importance, but also statistics of higher order. For several types of statistics we present simulation results and conduct quantitative evaluations by comparing them to experimental data as obtained from test subjects. [Collapse]