Using WordNet relations and semantic classes in information retrieval tasks

In this paper we explore the use of semantic classes in an existing information retrieval system in order to improve its results. Thus, we use two different ontologies of semantic classes (WordNet domain and Basic Level Concepts) in order to re-rank the retrieved documents and obtain better recall and precision. Finally, we implement a new method for weighting the expanded terms taking into account the weights of the original query terms and their relations in WordNet with respect to the new ones (which have demonstrated to improve the results). The evaluation of these approaches was carried out in the CLEF Robust-WSD Task, obtaining an improvement of 1.8% in GMAP for the semantic classes approach and 10% in MAP employing the WordNet term weighting approach.

Finding additional semantic entity information for search engines

Entity-oriented search has become an essential component of modern search engines. It focuses on retrieving a list of entities or information about the specific entities instead of documents. In this paper, we study the problem of finding entity related information, referred to as attribute-value pairs, that play a significant role in searching target entities. We propose a novel decomposition framework combining reduced relations and the discriminative model, Conditional Random Field (CRF), for automatically finding entity-related attribute-value pairs from free text documents. This decomposition framework allows us to locate potential text fragments and identify the hidden semantics, in the form of attribute-value pairs for user queries. Empirical analysis shows that the decomposition framework outperforms pattern-based approaches due to its capability of effective integration of syntactic and semantic features.

The 4C spectrum of fundamental behavioral relations for concurrent systems

The design of concurrent software systems, in particular process-aware information systems, involves behavioral modeling at various stages. Recently, approaches to behavioral analysis of such systems have been based on declarative abstractions defined as sets of behavioral relations. However, these relations are typically defined in an ad-hoc manner. In this paper, we address the lack of a systematic exploration of the fundamental relations that can be used to capture the behavior of concurrent systems, i.e., co-occurrence, conflict, causality, and concurrency. Besides the definition of the spectrum of behavioral relations, which we refer to as the 4C spectrum, we also show that our relations give rise to implication lattices. We further provide operationalizations of the proposed relations, starting by proposing techniques for computing relations in unlabeled systems, which are then lifted to become applicable in the context of labeled systems, i.e., systems in which state transitions have semantic annotations. Finally, we report on experimental results on efficiency of the proposed computations.

On Chinese Modal Particle A (啊) : A Pragmatic and Semantic Study

Chinese modal particles feature prominently in Chinese people’s daily use of the language, but their pragmatic and semantic functions are elusive as commonly recognised by Chinese linguists and teachers of Chinese as a foreign language. This book originates from an extensive and intensive empirical study of the Chinese modal particle a (啊), one of the most frequently used modal particles in Mandarin Chinese. In order to capture all the uses and the underlying meanings of the particle, the author transcribed the first 20 episodes, about 20 hours in length, of the popular Chinese TV drama series Kewang ‘Expectations’, which yielded a corpus data of more than 142’000 Chinese characters with a total of 1829 instances of the particle all used in meaningful communicative situations. Within its context of use, every single occurrence of the particle was analysed in terms of its pragmatic and semantic contributions to the hosting utterance. Upon this basis the core meanings were identified which were seen as constituting the modal nature of the particle.

Semantic spaces : measuring the distance between different subspaces

Semantic Space models, which provide a numerical representation of words’ meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.

Differential processing of thematic and categorical conceptual relations in spoken word production

Studies of semantic context effects in spoken word production have typically distinguished between categorical (or taxonomic) and associative relations. However, associates tend to confound semantic features or morphological representations, such as whole-part relations and compounds (e.g., BOAT-anchor, BEE-hive). Using a picture-word interference paradigm and functional magnetic resonance imaging (fMRI), we manipulated categorical (COW-rat) and thematic (COW-pasture) TARGET-distractor relations in a balanced design, finding interference and facilitation effects on naming latencies, respectively, as well as differential patterns of brain activation compared with an unrelated distractor condition. While both types of distractor relation activated the middle portion of the left middle temporal gyrus (MTG) consistent with retrieval of conceptual or lexical representations, categorical relations involved additional activation of posterior left MTG, consistent with retrieval of a lexical cohort. Thematic relations involved additional activation of the left angular gyrus. These results converge with recent lesion evidence implicating the left inferior parietal lobe in processing thematic relations and may indicate a potential role for this region during spoken word production.

Interference from object part relations in spoken word production: Behavioural and fMRI evidence

Objects presented in categorically related contexts are typically named slower than objects presented in unrelated contexts, a phenomenon termed semantic interference. However, not all semantic relationships induce interference. In the present study, we investigated the influence of object part-relations in the blocked cyclic naming paradigm. In Experiment 1 we established that an object's parts do induce a semantic interference effect when named in context compared to unrelated parts (e.g., leaf, root, nut, bark; for tree). In Experiment 2) we replicated the effect during perfusion functional magnetic resonance imaging (fMRI) to identify the cerebral regions involved. The interference effect was associated with significant perfusion signal increases in the hippocampal formation and decreases in the dorsolateral prefrontal cortex. We failed to observe significant perfusion signal changes in the left lateral temporal lobe, a region that shows reliable activity for interference effects induced by categorical relations in the same paradigm and is proposed to mediate lexical-semantic processing. We interpret these results as supporting recent explanations of semantic interference in blocked cyclic naming that implicate working memory mechanisms. However, given the failure to observe significant perfusion signal changes in the left temporal lobe, the results provide only partial support for accounts that assume semantic interference in this paradigm arises solely due to lexical-level processes.

Explorations in the distributional and semantic similarity of words

A straightforward computation of the list of the words (the `tail words' of the list) that are distributionally most similar to a given word (the `head word' of the list) leads to the question: How semantically similar to the head word are the tail words; that is: how similar are their meanings to its meaning? And can we do better? The experiment was done on nearly 18,000 most frequent nouns in a Finnish newsgroup corpus. These nouns are considered to be distributionally similar to the extent that they occur in the same direct dependency relations with the same nouns, adjectives and verbs. The extent of the similarity of their computational representations is quantified with the information radius. The semantic classification of head-tail pairs is intuitive; some tail words seem to be semantically similar to the head word, some do not. Each such pair is also associated with a number of further distributional variables. Individually, their overlap for the semantic classes is large, but the trained classification-tree models have some success in using combinations to predict the semantic class. The training data consists of a random sample of 400 head-tail pairs with the tail word ranked among the 20 distributionally most similar to the head word, excluding names. The models are then tested on a random sample of another 100 such pairs. The best success rates range from 70% to 92% of the test pairs, where a success means that the model predicted my intuitive semantic class of the pair. This seems somewhat promising when distributional similarity is used to capture semantically similar words. This analysis also includes a general discussion of several different similarity formulas, arranged in three groups: those that apply to sets with graded membership, those that apply to the members of a vector space, and those that apply to probability mass functions.

Mapping the semantic structure of cognitive neuroscience.

Cognitive neuroscience, as a discipline, links the biological systems studied by neuroscience to the processing constructs studied by psychology. By mapping these relations throughout the literature of cognitive neuroscience, we visualize the semantic structure of the discipline and point to directions for future research that will advance its integrative goal. For this purpose, network text analyses were applied to an exhaustive corpus of abstracts collected from five major journals over a 30-month period, including every study that used fMRI to investigate psychological processes. From this, we generate network maps that illustrate the relationships among psychological and anatomical terms, along with centrality statistics that guide inferences about network structure. Three terms--prefrontal cortex, amygdala, and anterior cingulate cortex--dominate the network structure with their high frequency in the literature and the density of their connections with other neuroanatomical terms. From network statistics, we identify terms that are understudied compared with their importance in the network (e.g., insula and thalamus), are underspecified in the language of the discipline (e.g., terms associated with executive function), or are imperfectly integrated with other concepts (e.g., subdisciplines like decision neuroscience that are disconnected from the main network). Taking these results as the basis for prescriptive recommendations, we conclude that semantic analyses provide useful guidance for cognitive neuroscience as a discipline, both by illustrating systematic biases in the conduct and presentation of research and by identifying directions that may be most productive for future research.

Strudel: A corpus-based semantic model based on properties and types

Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002; Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part-of-speech-tagged corpus. Concepts are characterized by weighted properties, enriched with concept-property types that approximate classical relations such as hypernymy and function. Our model outperforms comparable algorithms in cognitive tasks pertaining not only to concept-internal structures (discovering properties of concepts, grouping properties by property type) but also to inter-concept relations (clustering into superordinates), suggesting the empirical validity of the property-based approach. Copyright © 2009 Cognitive Science Society, Inc. All rights reserved.

Fostering e-participation sustainability through a BPM-driven semantic model

According to a recent Eurobarometer survey (2014), 68% of Europeans tend not to trust national governments. As the increasing alienation of citizens from politics endangers democracy and welfare, governments, practitioners and researchers look for innovative means to engage citizens in policy matters. One of the measures intended to overcome the so-called democratic deficit is the promotion of civic participation. Digital media proliferation offers a set of novel characteristics related to interactivity, ubiquitous connectivity, social networking and inclusiveness that enable new forms of societal-wide collaboration with a potential impact on leveraging participative democracy. Following this trend, e-Participation is an emerging research area that consists in the use of Information and Communication Technologies to mediate and transform the relations among citizens and governments towards increasing citizens’ participation in public decision-making. However, despite the widespread efforts to implement e-Participation through research programs, new technologies and projects, exhaustive studies on the achieved outcomes reveal that it has not yet been successfully incorporated in institutional politics. Given the problems underlying e-Participation implementation, the present research suggested that, rather than project-oriented efforts, the cornerstone for successfully implementing e-Participation in public institutions as a sustainable added-value activity is a systematic organisational planning, embodying the principles of open-governance and open-engagement. It further suggested that BPM, as a management discipline, can act as a catalyst to enable the desired transformations towards value creation throughout the policy-making cycle, including political, organisational and, ultimately, citizen value. Following these findings, the primary objective of this research was to provide an instrumental model to foster e-Participation sustainability across Government and Public Administration towards a participatory, inclusive, collaborative and deliberative democracy. The developed artefact, consisting in an e-Participation Organisational Semantic Model (ePOSM) underpinned by a BPM-steered approach, introduces this vision. This approach to e-Participation was modelled through a semi-formal lightweight ontology stack structured in four sub-ontologies, namely e-Participation Strategy, Organisational Units, Functions and Roles. The ePOSM facilitates e-Participation sustainability by: (1) Promoting a common and cross-functional understanding of the concepts underlying e-Participation implementation and of their articulation that bridges the gap between technical and non-technical users; (2) Providing an organisational model which allows a centralised and consistent roll-out of strategy-driven e-Participation initiatives, supported by operational units dedicated to the execution of transformation projects and participatory processes; (3) Providing a standardised organisational structure, goals, functions and roles related to e-Participation processes that enhances process-level interoperability among government agencies; (4) Providing a representation usable in software development for business processes’ automation, which allows advanced querying using a reasoner or inference engine to retrieve concrete and specific information about the e-Participation processes in place. An evaluation of the achieved outcomes, as well a comparative analysis with existent models, suggested that this innovative approach tackling the organisational planning dimension can constitute a stepping stone to harness e-Participation value.

Modèle de structuration des relations lexicales fondé sur le formalisme des fonctions lexicales

Thèse réalisée en cotutelle avec l'Université Paris Diderot (Paris 7)

Approche psycholinguistique des relations entre le traitement sémantique des mots et la rétention à court terme chez l’individu sain et dans la démence de type Alzheimer

Depuis ces deux dernières décennies, des efforts considérables en psychologie cognitive et neuropsychologie ont été déployés pour mieux comprendre les dynamiques entre les différents systèmes cognitifs (fonctions exécutives, mémoire, langage, etc.). L’observation d’une co-existence entre des atteintes en rétention à court terme verbale (RCTv) et en langage dans plusieurs pathologies a conduit certains chercheurs à concevoir une alternative à la conceptualisation classique de la RCTv, telle que proposée par le modèle initial de la mémoire de travail de Baddeley (1986). Particulièrement, les travaux sur l’aphasie ont conduit à l’émergence d’une approche psycholinguistique de la RCTv, postulant que le traitement et le maintien des mots font appel à des processus communs. Cette approche, et particulièrement le modèle d’activation interactive (N. Martin & Gupta, 2004; N. Martin & Saffran, 1997), prédit que les capacités en RCTv sont notamment modulées par les caractéristiques linguistiques engagées durant l’épreuve, variant selon la nature des items verbaux et la modalité de rappel, ainsi que par les compétences linguistiques des individus. L’objectif de la présente thèse était de tester ces prédictions à partir d’une exploration des relations entre le traitement sémantique des mots et la RCTv chez l’adulte sain (article 1) et dans la démence de type Alzheimer (DTA) (article 2). Dans le premier article, deux expériences mettent en évidence l’influence des liens associatifs entre les mots sur les capacités à maintenir temporairement des informations verbales. Les participants ont tendance à faussement reconnaître des mots associés aux mots de la liste, reflet d’une activation du réseau sémantique durant la rétention temporaire. Cette contribution sémantique est accentuée en situation de suppression articulatoire, une condition qui empêche le participant de répéter les listes durant la tâche. Les résultats du second article indiquent que la modalité de réponse module différemment la performance en RCTv chez les participants âgés sans atteinte neurologique et ceux atteints de la DTA. Ces données en RCTv sont compatibles avec les atteintes spécifiques du traitement du mot, également trouvées chez le groupe avec DTA. Les implications théoriques et cliniques de ces résultats sont discutées. Les limites et perspectives futures sont également abordées.

Relations entre morphologie, vocabulaire et compréhension écrite en français langue seconde

La présente étude vise à étayer l’état des connaissances liées aux relations entre les compétences en compréhension écrite, le vocabulaire et les capacités en traitement graphomorphologique d’étudiants collégiaux du français langue seconde de niveau intermédiaire et intermédiaire avancé. Bon nombre de ces étudiants présentent des problèmes de compréhension écrite, problèmes qui sont abordés dans la présente recherche sous l’angle des connaissances lexicales et du traitement graphomorphologique, soit la capacité de coordonner les informations de nature orthographique, phonologique et sémantique au cours de la lecture. Les résultats montrent que les étudiants possèdent de faibles connaissances lexicales, ce qui pourrait expliquer en partie les problèmes de compréhension écrite qu’ils présentent. Par ailleurs, même si les étudiants de niveau intermédiaire avancé affichent une meilleure compétence en compréhension écrite et de meilleures connaissances en vocabulaire que les étudiants de niveau intermédiaire, leurs capacités en matière de traitement graphomorphologique sont de même niveau. Enfin, les liens entre les trois variables semblent plus forts pour les étudiants de niveau intermédiaire que pour ceux de niveau intermédiaire avancé, ce qui pourrait laisser penser que le traitement graphomorphologique jouerait un rôle plus important pour les apprenants moins avancés d’une langue seconde.

FTMOntology: An ontology to fill the semantic gap between music, mood, personality, and human physiology

This paper presents a domain ontology, the FeelingTheMusic Ontology - FTMOntology. FTMOntology is designed to represent the complex domain of music and how it relates to other domains like mood, personality and physiology. This includes representing the main concepts and relations of music domain with each of the above-mentioned domains. The concepts and relations between music, mood, personality and physiology. The main contribution of this work is to model and relate these different domains in a consistent ontology. © 2011 Springer-Verlag.