Real, complex, and binary semantic vectors

This paper presents a combined structure for using real, complex, and binary valued vectors for semantic representation. The theory, implementation, and application of this structure are all significant. For the theory underlying quantum interaction, it is important to develop a core set of mathematical operators that describe systems of information, just as core mathematical operators in quantum mechanics are used to describe the behavior of physical systems. The system described in this paper enables us to compare more traditional quantum mechanical models (which use complex state vectors), alongside more generalized quantum models that use real and binary vectors. The implementation of such a system presents fundamental computational challenges. For large and sometimes sparse datasets, the demands on time and space are different for real, complex, and binary vectors. To accommodate these demands, the Semantic Vectors package has been carefully adapted and can now switch between different number types comparatively seamlessly. This paper describes the key abstract operations in our semantic vector models, and describes the implementations for real, complex, and binary vectors. We also discuss some of the key questions that arise in the field of quantum interaction and informatics, explaining how the wide availability of modelling options for different number fields will help to investigate some of these questions.

Semantic judgement of medical concepts : combining syntagmatic and paradigmatic information with the tensor encoding model

This paper outlines a novel approach for modelling semantic relationships within medical documents. Medical terminologies contain a rich source of semantic information critical to a number of techniques in medical informatics, including medical information retrieval. Recent research suggests that corpus-driven approaches are effective at automatically capturing semantic similarities between medical concepts, thus making them an attractive option for accessing semantic information. Most previous corpus-driven methods only considered syntagmatic associations. In this paper, we adapt a recent approach that explicitly models both syntagmatic and paradigmatic associations. We show that the implicit similarity between certain medical concepts can only be modelled using paradigmatic associations. In addition, the inclusion of both types of associations overcomes the sensitivity to the training corpus experienced by previous approaches, making our method both more effective and more robust. This finding may have implications for researchers in the area of medical information retrieval.

New discursive configurations of journalism : Narratives in Rio de Janeiro's favelas [Novas configurações discursivas no jornalismo: narrativas digitais nas favelas do Rio de Janeiro]

Oprincipal objetivo desse artigo é apresentar os resultados parciais de uma pesquisa em andamento sobre o processo de produção de conteúdo do portal Viva Favela, um dos projetos sociais realizados pela organização nãogovernamental Viva Rio. Partindo de uma abordagem conceitual que discute os modos pelos quais a mídia alternativa e o jornalismo público/jornalismo cívico criam as condições de possibilidade para que uma determinada prática jornalística dê ‘voz’ e ‘empodere’ (empower) moradores de periferias e favelas brasileiras, estamos realizando um estudo das rotinas produtivas do Viva Favela e seus ‘correspondentes comunitários’. O conceito sobre voice, de Jo Tacchi, oferece-nos um embasamento teórico adequado para refletirmos sobre o que vem sendo denominado, nos Estados Unidos, de digital storytelling – as narrativas digitais produzidas com as tecnologias de informação e comunicação para “contar estórias” 1, que são criativamente apropriadas, no Brasil, por moradores das favelas e periferias das regiões metropolitanas.

Empirical analysis of the effect of dimension reduction and word order on semantic vectors

The aim of this paper is to provide a comparison of various algorithms and parameters to build reduced semantic spaces. The effect of dimension reduction, the stability of the representation and the effect of word order are examined in the context of the five algorithms bearing on semantic vectors: Random projection (RP), singular value decom- position (SVD), non-negative matrix factorization (NMF), permutations and holographic reduced representations (HRR). The quality of semantic representation was tested by means of synonym finding task using the TOEFL test on the TASA corpus. Dimension reduction was found to improve the quality of semantic representation but it is hard to find the optimal parameter settings. Even though dimension reduction by RP was found to be more generally applicable than SVD, the semantic vectors produced by RP are somewhat unstable. The effect of encoding word order into the semantic vector representation via HRR did not lead to any increase in scores over vectors constructed from word co-occurrence in context information. In this regard, very small context windows resulted in better semantic vectors for the TOEFL test.

Social moralities and discursive constructions of female sex offenders

This article explores legal, scholarly and social responses to women identified as sex offenders. While much has been written on the male paedophile, rapist and sex offender, little research has been done on the role of gender and sexuality in sex offending. This article examines the ways in which the female sex offender is currently theorized and the discourses surrounding policy, legislative and media responses to their crimes. We identify contradictory public discourses where perceptions of female child abusers in particular often succumb to moral panic, in spite of many such offenders being given lenient sentences for their crimes. An examination of the discursive construction of female child abusers suggests that these contradictions are informed by underlying assumptions concerning harm and subjectivity in sex crimes. In exploring these contradictions we illustrate the ways in which such discourses are impacted by social moralities, and how social moralities construct offender and victim subjectivities differently, based on differences in gender, age and sexuality.

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.

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.

Semantic labelling for document feature patterns using ontological subjects

Finding and labelling semantic features patterns of documents in a large, spatial corpus is a challenging problem. Text documents have characteristics that make semantic labelling difficult; the rapidly increasing volume of online documents makes a bottleneck in finding meaningful textual patterns. Aiming to deal with these issues, we propose an unsupervised documnent labelling approach based on semantic content and feature patterns. A world ontology with extensive topic coverage is exploited to supply controlled, structured subjects for labelling. An algorithm is also introduced to reduce dimensionality based on the study of ontological structure. The proposed approach was promisingly evaluated by compared with typical machine learning methods including SVMs, Rocchio, and kNN.

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.

Nonparametric semantic segmentation for 3D street scenes

In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth.

Mapping semantic knowledge for unsupervised text categorisation

Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts.

White teachers at a discursive crossroad

This study explored the way white teachers speak about Indigenous students and communities. The accounts of teachers working in eight schools across Australia were analysed using Foucauldian discourse analysis. The research found that deficit and colour-blind discourses dominate the ways that white teachers "know" Indigenous students and families. The data indicates that colour-blind and compensatory pedagogies are employed heavily by teachers working with Indigenous students, and highlights the complexities and tensions that exist in schools. Although there is evidence of some disruption to dominant discourses, teachers' discursive resources are limited in terms of imagining and enacting more equitable pedagogies.

The discursive nature of citizenship : Indigenous sovereign rights, racism and welfare reform

Citizenship is more than a status associated with a bundle of rights; it is also the formal contract by which the sovereignty of a nation is extended to the individual in exchange for being governed. Who can and who cannot contract into this status and what rights are able to be exercised is also shaped by who possesses the nation. In this article it is argued that citizenship operates discursively to contain Indigenous people’s engagement with the economy through social rights. This containment precludes consideration of Indigenous sovereign rights to our lands and resources, to enable Indigenous economic development within a capitalist market economy.

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.

Continuity, Rupture, or just more of the 'Volatile and Contradictory': Glimpses of New South Wales' Penal Practice behind and through the Discursive

Throughout much of the western world more and more people are being sent to prison, one of a number of changes inspired by a 'new punitiveness' in penal and political affairs. This book seeks to understand these developments, bringing together leading authorities in the field to provide a wide-ranging analysis of new penal trends, compare the development of differing patterns of punishment across different types of societies, and to provide a range of theoretical analyses and commentaries to help understand their significance. As well as increases in imprisonment this book is also concerned to address a number of other aspects of 'the new punitiveness': firstly, the return of a number of forms of punishment previously thought extinct or inappropriate, such as the return of shaming punishments and chain gangs (in parts of the USA); and secondly, the increasing public involvement in penal affairs and penal development, for example in relation to length of sentences and the California Three Strikes Law, and a growing accreditation of the rights of victims. The book will be essential reading for students seeking to understand trends and theories of punishment on law, criminology, penology and other courses.