Modelling word activation in semantic networks : three scaled entanglement models compared

Modelling how a word is activated in human memory is an important requirement for determining the probability of recall of a word in an extra-list cueing experiment. Previous research assumed a quantum-like model in which the semantic network was modelled as entangled qubits, however the level of activation was clearly being over-estimated. This paper explores three variations of this model, each of which are distinguished by a scaling factor designed to compensate the overestimation.

Turning the tide: A critique of Natural Semantic Metalanguage from a translation studies perspective

Starting from the premise that human communication is predicated on translational phenomena, this paper applies theoretical insights and practical findings from Translation Studies to a critique of Natural Semantic Metalanguage (NSM), a theory of semantic analysis developed by Anna Wierzbicka. Key tenets of NSM, i.e. (1) culture-specificity of complex concepts; (2) the existence of a small set of universal semantic primes; and (3) definition by reductive paraphrase, are discussed critically with reference to the notions of untranslatability, equivalence, and intra-lingual translation, respectively. It is argued that a broad spectrum of research and theoretical reflection in Translation Studies may successfully feed into the study of cognition, meaning, language, and communication. The interdisciplinary exchange between Translation Studies and linguistics may be properly balanced, with the former not only being informed by but also informing and interrogating the latter.

Comparing the semantic networks of deaf and hearing children using the DRM paradigm

This study uses the Deese-Roediger-McDermott paradigm to investigate how deaf children with cochlear implants organize their semantic networks as compared to their hearing age-mates.

Combining temporal and spectral information with spatial mapping to identify differences between phonological and semantic networks:a magnetoencephalographic approach

Early, lesion-based models of language processing suggested that semantic and phonological processes are associated with distinct temporal and parietal regions respectively, with frontal areas more indirectly involved. Contemporary spatial brain mapping techniques have not supported such clear-cut segregation, with strong evidence of activation in left temporal areas by both processes and disputed evidence of involvement of frontal areas in both processes. We suggest that combining spatial information with temporal and spectral data may allow a closer scrutiny of the differential involvement of closely overlapping cortical areas in language processing. Using beamforming techniques to analyze magnetoencephalography data, we localized the neuronal substrates underlying primed responses to nouns requiring either phonological or semantic processing, and examined the associated measures of time and frequency in those areas where activation was common to both tasks. Power changes in the beta (14-30 Hz) and gamma (30-50 Hz) frequency bandswere analyzed in pre-selected time windows of 350-550 and 500-700ms In left temporal regions, both tasks elicited power changes in the same time window (350-550 ms), but with different spectral characteristics, low beta (14-20 Hz) for the phonological task and high beta (20-30 Hz) for the semantic task. In frontal areas (BA10), both tasks elicited power changes in the gamma band (30-50 Hz), but in different time windows, 500-700ms for the phonological task and 350-550ms for the semantic task. In the left inferior parietal area (BA40), both tasks elicited changes in the 20-30 Hz beta frequency band but in different time windows, 350-550ms for the phonological task and 500-700ms for the semantic task. Our findings suggest that, where spatial measures may indicate overlapping areas of involvement, additional beamforming techniques can demonstrate differential activation in time and frequency domains. © 2012 McNab, Hillebrand, Swithenby and Rippon.

Conhecimento sobre energia eólica: um estudo exploratório a partir das redes semânticas naturais de estudantes da cidade de Natal

The human being has a fundamental role in issues concerning scarcity of energy resources as well as in the success of technologies which favor the use of renewable sources, such as wind energy. But what does wind energy mean for people? What are the psychological meanings concerning this term? Aiming to answer these questions, the general objective of this dissertation was to identify and to analyze the knowledge about wind energy taking into account a network of psychological meanings. One hundred and ninety one (191) university students from Natal-RN participated in the study, being male the majority (53%); aged between 17 and 51 years old (M=23.3 years; SD=5.7). Participants responded to self reports using the Natural Semantic Networks (NSW) technique, as well as to several sociodemographic questions. The results showed a consistent, positive and useful general semantic network. In this semantic network, knowledge about wind energy was represented by words that correspond to the appeal of (pro) environmental stance (e.g., clean and nature), that evinced both the technological aspect (e.g., force) and the economic aspect (e.g., economy). Results from different groups were also analyzed. The first set of groups (non-environemental and environmental) was divided considering the course (e.g., ecology and economy). The second set of groups(non-caretaker and caretaker) was divided based on the practice of environmental care expressed. Subtle differences were observed in the semantic networks of caretakers, who emphasized environment, an attribute not mentioned by non-caretakes. This indicates a construction of knowledge that is influenced by the presence or absence of the environmental commitment. Such findings may be useful in the construction of instruments for surveys and in the development of public and educational policies. Additionally, they may assist the media towards a more objective performance concerning wind energy

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

A tensor encoding model for semantic processing

This paper develops and evaluates an enhanced corpus based approach for semantic processing. Corpus based models that build representations of words directly from text do not require pre-existing linguistic knowledge, and have demonstrated psychologically relevant performance on a number of cognitive tasks. However, they have been criticised in the past for not incorporating sufficient structural information. Using ideas underpinning recent attempts to overcome this weakness, we develop an enhanced tensor encoding model to build representations of word meaning for semantic processing. Our enhanced model demonstrates superior performance when compared to a robust baseline model on a number of semantic processing tasks.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Deriving word association networks from text corpora

This article presents and evaluates a model to automatically derive word association networks from text corpora. Two aspects were evaluated: To what degree can corpus-based word association networks (CANs) approximate human word association networks with respect to (1) their ability to quantitatively predict word associations and (2) their structural network characteristics. Word association networks are the basis of the human mental lexicon. However, extracting such networks from human subjects is laborious, time consuming and thus necessarily limited in relation to the breadth of human vocabulary. Automatic derivation of word associations from text corpora would address these limitations. In both evaluations corpus-based processing provided vector representations for words. These representations were then employed to derive CANs using two measures: (1) the well known cosine metric, which is a symmetric measure, and (2) a new asymmetric measure computed from orthogonal vector projections. For both evaluations, the full set of 4068 free association networks (FANs) from the University of South Florida word association norms were used as baseline human data. Two corpus based models were benchmarked for comparison: a latent topic model and latent semantic analysis (LSA). We observed that CANs constructed using the asymmetric measure were slightly less effective than the topic model in quantitatively predicting free associates, and slightly better than LSA. The structural networks analysis revealed that CANs do approximate the FANs to an encouraging degree.

Construção linguístico-computacional de um ontoglossário inglês-português do domínio léxico-conceitual LOVE LIFE

Pós-graduação em Linguística e Língua Portuguesa - FCLAR

Loose but normal: a semantic association study.

An abnormal facilitation of the spreading activation within semantic networks is thought to under-lie schizophrenics' remote associations and referential ideas. In normal subjects, elevated magical ideation (MI) has also been associated with a style of thinking similar to that of schizotypal subjects. We thus wondered whether normal subjects with a higher MI score would judge "loose associations" as being more closely related than do subjects with a lower MI score. In two experiments, we investigated whether judgments of the semantic distance between stimulus words varied as a function of MI. In the first experiment, random word pairs of two word classes, animals and fruits, were presented. Subjects had to judge the semantic distance between word pairs. In the second experiment, sets of three words were presented, consisting of a pair of indirectly related, or unrelated nouns plus a third noun. Subjects had to judge the semantic distance of the third noun to the word pair The results of both experiments showed that higher MI subjects considered unrelated words as more closely associated than did lower MI subjects. We conjecture that for normal subjects high on MI "loose associations" may not be loose after all. We also note that the tendency to link uncommon, nonobvious, percepts may not only be the basis of paranormal and paranoid ideas of reference, but also a prerequisite of creative thinking.

Método para la Construcción Automática de Ontologías Basado en Patrones Lingüísticos

La tesis que se presenta tiene como propósito la construcción automática de ontologías a partir de textos, enmarcándose en el área denominada Ontology Learning. Esta disciplina tiene como objetivo automatizar la elaboración de modelos de dominio a partir de fuentes información estructurada o no estructurada, y tuvo su origen con el comienzo del milenio, a raíz del crecimiento exponencial del volumen de información accesible en Internet. Debido a que la mayoría de información se presenta en la web en forma de texto, el aprendizaje automático de ontologías se ha centrado en el análisis de este tipo de fuente, nutriéndose a lo largo de los años de técnicas muy diversas provenientes de áreas como la Recuperación de Información, Extracción de Información, Sumarización y, en general, de áreas relacionadas con el procesamiento del lenguaje natural. La principal contribución de esta tesis consiste en que, a diferencia de la mayoría de las técnicas actuales, el método que se propone no analiza la estructura sintáctica superficial del lenguaje, sino que estudia su nivel semántico profundo. Su objetivo, por tanto, es tratar de deducir el modelo del dominio a partir de la forma con la que se articulan los significados de las oraciones en lenguaje natural. Debido a que el nivel semántico profundo es independiente de la lengua, el método permitirá operar en escenarios multilingües, en los que es necesario combinar información proveniente de textos en diferentes idiomas. Para acceder a este nivel del lenguaje, el método utiliza el modelo de las interlinguas. Estos formalismos, provenientes del área de la traducción automática, permiten representar el significado de las oraciones de forma independiente de la lengua. Se utilizará en concreto UNL (Universal Networking Language), considerado como la única interlingua de propósito general que está normalizada. La aproximación utilizada en esta tesis supone la continuación de trabajos previos realizados tanto por su autor como por el equipo de investigación del que forma parte, en los que se estudió cómo utilizar el modelo de las interlinguas en las áreas de extracción y recuperación de información multilingüe. Básicamente, el procedimiento definido en el método trata de identificar, en la representación UNL de los textos, ciertas regularidades que permiten deducir las piezas de la ontología del dominio. Debido a que UNL es un formalismo basado en redes semánticas, estas regularidades se presentan en forma de grafos, generalizándose en estructuras denominadas patrones lingüísticos. Por otra parte, UNL aún conserva ciertos mecanismos de cohesión del discurso procedentes de los lenguajes naturales, como el fenómeno de la anáfora. Con el fin de aumentar la efectividad en la comprensión de las expresiones, el método provee, como otra contribución relevante, la definición de un algoritmo para la resolución de la anáfora pronominal circunscrita al modelo de la interlingua, limitada al caso de pronombres personales de tercera persona cuando su antecedente es un nombre propio. El método propuesto se sustenta en la definición de un marco formal, que ha debido elaborarse adaptando ciertas definiciones provenientes de la teoría de grafos e incorporando otras nuevas, con el objetivo de ubicar las nociones de expresión UNL, patrón lingüístico y las operaciones de encaje de patrones, que son la base de los procesos del método. Tanto el marco formal como todos los procesos que define el método se han implementado con el fin de realizar la experimentación, aplicándose sobre un artículo de la colección EOLSS “Encyclopedia of Life Support Systems” de la UNESCO. ABSTRACT The purpose of this thesis is the automatic construction of ontologies from texts. This thesis is set within the area of Ontology Learning. This discipline aims to automatize domain models from structured or unstructured information sources, and had its origin with the beginning of the millennium, as a result of the exponential growth in the volume of information accessible on the Internet. Since most information is presented on the web in the form of text, the automatic ontology learning is focused on the analysis of this type of source, nourished over the years by very different techniques from areas such as Information Retrieval, Information Extraction, Summarization and, in general, by areas related to natural language processing. The main contribution of this thesis consists of, in contrast with the majority of current techniques, the fact that the method proposed does not analyze the syntactic surface structure of the language, but explores his deep semantic level. Its objective, therefore, is trying to infer the domain model from the way the meanings of the sentences are articulated in natural language. Since the deep semantic level does not depend on the language, the method will allow to operate in multilingual scenarios, where it is necessary to combine information from texts in different languages. To access to this level of the language, the method uses the interlingua model. These formalisms, coming from the area of machine translation, allow to represent the meaning of the sentences independently of the language. In this particular case, UNL (Universal Networking Language) will be used, which considered to be the only interlingua of general purpose that is standardized. The approach used in this thesis corresponds to the continuation of previous works carried out both by the author of this thesis and by the research group of which he is part, in which it is studied how to use the interlingua model in the areas of multilingual information extraction and retrieval. Basically, the procedure defined in the method tries to identify certain regularities at the UNL representation of texts that allow the deduction of the parts of the ontology of the domain. Since UNL is a formalism based on semantic networks, these regularities are presented in the form of graphs, generalizing in structures called linguistic patterns. On the other hand, UNL still preserves certain mechanisms of discourse cohesion from natural languages, such as the phenomenon of the anaphora. In order to increase the effectiveness in the understanding of expressions, the method provides, as another significant contribution, the definition of an algorithm for the resolution of pronominal anaphora limited to the model of the interlingua, in the case of third person personal pronouns when its antecedent is a proper noun. The proposed method is based on the definition of a formal framework, adapting some definitions from Graph Theory and incorporating new ones, in order to locate the notions of UNL expression and linguistic pattern, as well as the operations of pattern matching, which are the basis of the method processes. Both the formal framework and all the processes that define the method have been implemented in order to carry out the experimentation, applying on an article of the "Encyclopedia of Life Support Systems" of the UNESCO-EOLSS collection.