Incremental Construction of an Associative Network from a Corpus

This paper presents a computational model of the incremental construction of an associative network from a corpus. It is aimed at modeling the development of the human semantic memory. It is not based on a vector representation, which does not well reproduce the asymmetrical property of word similarity, but rather on a network representation. Compared to Latent Semantic Analysis, it is incremental which is cognitively more plausible. It is also an attempt to take into account higher-order co-occurrences in the construction of word similarities. This model was compared to children association norms. A good correlation as well as a similar gradient of similarity were found

A Computational Model of Children's Semantic Memory

A computational model of children's semantic memory is built from the Latent Semantic Analysis (LSA) of a multisource child corpus. Three tests of the model are described, simulating a vocabulary test, an association test and a recall task. For each one, results from experiments with children are presented and compared to the model data. Adequacy is correct, which means that this simulation of children's semantic memory can be used to simulate a variety of children's cognitive processes

Quantum Aspects of Semantic Analysis and Symbolic Artificial Intelligence

Modern approaches to semanic analysis if reformulated as Hilbert-space problems reveal formal structures known from quantum mechanics. Similar situation is found in distributed representations of cognitive structures developed for the purposes of neural networks. We take a closer look at similarites and differences between the above two fields and quantum information theory.Comment: version accepted in J. Phys. A (Letter to the Editor

Meaning-focused and Quantum-inspired Information Retrieval

In recent years, quantum-based methods have promisingly integrated the traditional procedures in information retrieval (IR) and natural language processing (NLP). Inspired by our research on the identification and application of quantum structures in cognition, more specifically our work on the representation of concepts and their combinations, we put forward a 'quantum meaning based' framework for structured query retrieval in text corpora and standardized testing corpora. This scheme for IR rests on considering as basic notions, (i) 'entities of meaning', e.g., concepts and their combinations and (ii) traces of such entities of meaning, which is how documents are considered in this approach. The meaning content of these 'entities of meaning' is reconstructed by solving an 'inverse problem' in the quantum formalism, consisting of reconstructing the full states of the entities of meaning from their collapsed states identified as traces in relevant documents. The advantages with respect to traditional approaches, such as Latent Semantic Analysis (LSA), are discussed by means of concrete examples.Comment: 11 page

Understanding Individual Experiences of Chronic Illness with Semantic Space Models of Electronic Discussions

Electronic discussion groups provide a convenient forum for individuals to share their experiences of chronic illness. The language use of individual participants, and the way their language shifts over time, may provide implicit indications of important shifts in sense-of-self. This paper relates experience with application of the hyperspace analogue to language (HAL) model for automatic construction of a dimensional model from a corpus of text. HAL is applied to 17 months of discussion on a closed list of 20 women coping with chronic illness. The discussion group was moderated for a focus the phenomenon of "Transition' - how people can learn to incorporate the consequences of illness into their lives. The current phase of research focuses on identification of clusters of words that can represent key aspects of Transition. The HAL models for two participants have been analyzed by experts in Transition to form candidate clusters. These clusters are then used as a basis for contrasting the language usage of an individual participant over time as compared to the entire corpus. We have not yet found a reliable basis for identifying transitions in an individual based on their entries into a discussion forum, although the clusters may have some inherent value for introspection on individual experiences and Transition in general. We report challenges for interpretation of the HAL model related to the correlation of dimensions and the impact of group dynamics

Terminology mining in social media

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining

Word Embeddings: A Survey

This work lists and describes the main recent strategies for building fixed-length, dense and distributed representations for words, based on the distributional hypothesis. These representations are now commonly called word embeddings and, in addition to encoding surprisingly good syntactic and semantic information, have been proven useful as extra features in many downstream NLP tasks.Comment: 10 pages, 2 tables, 1 imag

Concept learning and information inferencing on a high-dimensional semantic space

How to automatically capture a significant portion of relevant background knowledge and keep it up-to-date has been a challenging problem encountered in current research on logic based information retrieval. This paper addresses this problem by investigating various information inference mechanisms based on a high dimensional semantic space constructed from a text corpus using the Hyperspace Analogue to Language (HAL) model. Additionally, the Singular Value Decomposition (SVD) algorithm is considered as an alternative way to enhance the quality of the HAL matrix as well as a mechanism of infering implicit associations. The different characteristics of these inference mechanisms are demonstrated using examples from the Reuters-21578 collection. Our hope is that the techniques discussed in this paper provide a basis for logic based IR to progress to large scale applications

Towards a quantum evolutionary scheme: violating Bell's inequalities in language

We show the presence of genuine quantum structures in human language. The neo-Darwinian evolutionary scheme is founded on a probability structure that satisfies the Kolmogorovian axioms, and as a consequence cannot incorporate quantum-like evolutionary change. In earlier research we revealed quantum structures in processes taking place in conceptual space. We argue that the presence of quantum structures in language and the earlier detected quantum structures in conceptual change make the neo-Darwinian evolutionary scheme strictly too limited for Evolutionary Epistemology. We sketch how we believe that evolution in a more general way should be implemented in epistemology and conceptual change, but also in biology, and how this view would lead to another relation between both biology and epistemology.Comment: 20 pages, no figures, this version of the paper is equal to the foregoing. The paper has meanwhile been published in another book series than the one tentatively mentioned in the comments given with the foregoing versio

Evaluation: Measurements of Differences between Semantic Spaces

The existing method to measure differences among semantic spaces is costly. The current study evaluates a low-cost method. Specifically, the current study uses three measurements of induced semantic structures (ISS) to measure the differences between vector-based semantic spaces. An ISS of a target word is that word\u27s ordered nearest neighbors. Our hypothesis, which was confirmed, is that the three measurements have the ability to measure the differences between spaces. In addition, the number of nearest neighbors used by measurements has an effect on the ability. Evaluation was conducted on five Touchstone Applied Science Associates (TASA) spaces. The measured differences between spaces were compared to the objective similar pattern of TASA spaces, which follow a well-defined hierarchy. The comparison indicateds that three measurements can capture the objective TASA pattern and that performance measures were better than a measurement which does not use ISS. It was concluded that the new method of measuring space differences in an apt complement to the existing method