A voting mechanism for named entity translation in English-Chinese question answering

In this paper, we describe a voting mechanism for accurate named entity (NE) translation in English–Chinese question answering (QA). This mechanism involves translations from three different sources: machine translation,online encyclopaedia, and web documents. The translation with the highest number of votes is selected. We evaluated this approach using test collection, topics and assessment results from the NTCIR-8 evaluation forum. This mechanism achieved 95% accuracy in NEs translation and 0.3756 MAP in English–Chinese cross-lingual information retrieval of QA.

Small business entity tax concessions : through the eyes of the practitioner

The often competing imperatives of equity, simplicity and efficiency in the income tax regime, particularly the notion of simplicity, has been most evident within Australia’s small business sector over the last decade. In an attempt to provide tax simplification and reduce the tax compliance burden faced by Australian small businesses, provisions collectively referred to as the ‘simplified tax system’ or STS were introduced. The STS was designed to provide eligible small businesses with the option of adopting a range of ‘simplified’ tax measures designed to simplify their tax affairs whilst at the same time, reducing their tax compliance costs. Ultimately, a low take-up rate and accompanying criticisms led to a remodelled and rebadged concessionary regime known as the ‘Small Business Entity’ (SBE) regime which came into effect from 1 July 2007. This paper, through a pilot study, investigates the SBE regime though the eyes of the practitioner. In line the Australian Federal Government’s objective of simplification and reduced compliance costs, the purpose of the study was to (1) determine the extent to which the SBE concessions are being adopted by tax practitioners on behalf of their clients, (2) gain an understanding as to which individual SBE tax concessions are most favoured by practitioners, (3) determine the primary motivation as to why tax practitioners recommend particular SBE concessions to their clients, and (4) canvass the opinions of practitioners as to whether they believed that the introduction of the SBE concessions had met their stated objective of reducing tax compliance costs for small businesses. The findings of this research indicate that, while there is a perception that the SBE concessions are worth embracing, contrary to the policy intent, the reasons behind adopting the concessions was the opportunity to minimise a clients’ tax liability. It was revealed that adopting particular concessions had nothing to do with compliance costs savings and, in fact, the SBE concessions merely added another layer of complexity to an already cumbersome and complex tax code, which resulted in increased compliance costs for their small businesses clients. Further, the SBE concessions allowed tax practitioners the opportunity to engage in effective tax minimisation, thereby fulfilling the client advocacy role of the tax practitioner in maximising their clients’ tax preferences.

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.

Small business entity tax concessions : through the eyes of the practitioner

Australia’s small business sector has pursued often-competing imperatives of simplicity, equity and efficiency in the income tax regime (particularly focusing on the notion of simplicity) over the last decade. In 2001, there was an attempt to provide such simplification and reduce the compliance burden faced by Australian small businesses through the ‘simplified tax system’ (‘STS’). However, despite amendments over the years, the regime is much criticised. This article explores how the STS (now known as the ‘small business entity’ regime or ‘SBE’) is utilised from the perspective of tax practitioners, by analysing their recommendations to small business clients in respect of the regime. The results indicate that practitioners believe the regime did nothing to simplify the tax system for small businesses or reduce tax compliance costs. Indeed, the practitioners believed that the introduction of small business concessions had actually achieved the opposite result — it had increased tax compliance costs for their small business clients. However, tax practitioners still recommend the regime highly because it minimises their client’s tax liability.

A concept-based retrieval method for entity-oriented search

Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not able to exploit the semantic intent by capturing the semantic relationship between terms in a query and in a document that contains entity related information. To improve the understanding of the semantic intent of user queries, we propose concept-based retrieval method that not only automatically identifies the semantic intent of user queries, i.e., Intent Type and Intent Modifier but introduces concepts represented by Wikipedia articles to user queries. We evaluate our proposed method on entity profile documents annotated by concepts from Wikipedia category and list structure. Empirical analysis reveals that the proposed method outperforms several state-of-the-art approaches.

An iterative speaker re-diarization scheme for improving speaker-based entity extraction in multimedia archives

In this paper we present a novel scheme for improving speaker diarization by making use of repeating speakers across multiple recordings within a large corpus. We call this technique speaker re-diarization and demonstrate that it is possible to reuse the initial speaker-linked diarization outputs to boost diarization accuracy within individual recordings. We first propose and evaluate two novel re-diarization techniques. We demonstrate their complementary characteristics and fuse the two techniques to successfully conduct speaker re-diarization across the SAIVT-BNEWS corpus of Australian broadcast data. This corpus contains recurring speakers in various independent recordings that need to be linked across the dataset. We show that our speaker re-diarization approach can provide a relative improvement of 23% in diarization error rate (DER), over the original diarization results, as well as improve the estimated number of speakers and the cluster purity and coverage metrics.

Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering

This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the performance of entity-oriented retrieval, identifies different lexical forms for an entity relation and handles clustering documents with multiple feature spaces.

Axial spondyloarthritis: A new disease entity, not necessarily early ankylosing spondylitis

New classification criteria for axial spondyloarthritis have been developed with the goal of increasing sensitivity of criteria for early inflammatory spondyloarthritis. However these criteria substantially increase heterogeneity of the resulting disease group, reducing their value in both research and clinical settings. Further research to establish criteria based on better knowledge of the natural history of non-radiographic axial spondyloarthritis, its aetiopathogenesis and response to treatment is required. In the meantime the modified New York criteria for ankylosing spondylitis remain a very useful classification criteria set, defining a relatively homogenous group of cases for clinical use and research studies.

GEODERM: geometric shape design system using an entity-relationship model

GEODERM, a microcomputer-based solid modeller, which incorporates the parametric object model, is discussed. The entity-relationship model, which is used to describe the conceptual schema of the geometric database, is also presented. Three of the four modules of GEODERM, which have been implemented are described in some detail. They are the Solid Definition Language (SDL), the Solid Manipulation Language (SML) and the User-System Interface.

Government entity contribution to an effective partnership privatisation

The partnership form of privatisation is increasingly being used, in particular to carry out complex and evolving bundles of services. These have not previously been privatised because of incomplete contracts and contract management difficulties. Improved performance of the government entity as contract administrator and member of the partnership is crucial to modern service delivery expectations yet the privatisation literature has focused on other aspects of partnerships leaving the understanding of factors impacting the effectiveness of the government entity underdeveloped. This paper proposes the development of knowledge as to the range of factors which impact the effectiveness of the government entity. There is limited data available as to the operation of trust in the partnership relationship, and as to the capability of a range of privatisation forms to achieve stewardship of infrastructure. This research will utilise the findings from that research to build a tentative framework which will be utilised in staged research interrogating first the privatization literature and then the literature of other disciplines and sectors. The combined data will be analysed to provide government and practitioners such as government entity CEO’s with a complete listing of the operation of the factors which impact the effectiveness of the government entity in contributing to improved service delivery.

Collective eigenstates of emission in an N-entity heterostructure and the evaluation of its Green tensors and self-energy components

Optical emission from emitters strongly interacting among themselves and also with other polarizable matter in close proximity has been approximated by emission from independent emitters. This is primarily due to our inability to evaluate the self-energy matrices and radiative properties of the collective eigenstates of emitters in heterogeneous ensembles. A method to evaluate self-energy matrices that is not limited by the geometry and material composition is presented to understand and exploit such collective excitations. Numerical evaluations using this method are used to highlight the significant differences between independent and the collective modes of emission in nanoscale heterostructures. A set of N Lorentz emitters and other polarizable entities is used to represent the coupled system of a generalized geometry in a volume integral approach. Closed form relations between the Green tensors of entity pairs in free space and their correspondents in a heterostructure are derived concisely. This is made possible for general geometries because the global matrices consisting of all free-space Green dyads are subject to conservation laws. The self-energy matrix can then be assembled using the evaluated Green tensors of the heterostructure, but a decomposition of its components into their radiative and nonradiative decay contributions is nontrivial. The relations to compute the observables of the eigenstates (such as quantum efficiency, power/energy of emission, radiative and nonradiative decay rates) are presented. A note on extension of this method to collective excitations, which also includes strong interactions with a surface in the near-field, is added. (C) 2014 Optical Society of America

Combining rimonabant and fentanyl in a single entity: preparation and pharmacological results

Based on numerous pharmacological studies that have revealed an interaction between cannabinoid and opioid systems at the molecular, neurochemical, and behavioral levels, a new series of hybrid molecules has been prepared by coupling the molecular features of two well-known drugs, ie, rimonabant and fentanyl. The new compounds have been tested for their affinity and functionality regarding CB1 and CB2 cannabinoid and mu opioid receptors. In [S-35]-GTP.S (guanosine 5'-O-[gamma-thio] triphosphate) binding assays from the post-mortem human frontal cortex, they proved to be CB1 cannabinoid antagonists and mu opioid antagonists. Interestingly, in vivo, the new compounds exhibited a significant dual antagonist action on the endocannabinoid and opioid systems.