Automated cross-lingual link discovery in Wikipedia

At NTCIR-9, we participated in the cross-lingual link discovery (Crosslink) task. In this paper we describe our approaches to discovering Chinese, Japanese, and Korean (CJK) cross-lingual links for English documents in Wikipedia. Our experimental results show that a link mining approach that mines the existing link structure for anchor probabilities and relies on the “translation” using cross-lingual document name triangulation performs very well. The evaluation shows encouraging results for our system.

Overview of the NTCIR-9 crosslink task : cross-lingual link discovery

This paper presents an overview of NTCIR-9 Cross-lingual Link Discovery (Crosslink) task. The overview includes: the motivation of cross-lingual link discovery; the Crosslink task definition; the run submission specification; the assessment and evaluation framework; the evaluation metrics; and the evaluation results of submitted runs. Cross-lingual link discovery (CLLD) is a way of automatically finding potential links between documents in different languages. The goal of this task is to create a reusable resource for evaluating automated CLLD approaches. The results of this research can be used in building and refining systems for automated link discovery. The task is focused on linking between English source documents and Chinese, Korean, and Japanese target documents.

The effectiveness of cross-lingual link discovery

This paper describes the evaluation in benchmarking the effectiveness of cross-lingual link discovery (CLLD). Cross lingual link discovery is a way of automatically finding prospective links between documents in different languages, which is particularly helpful for knowledge discovery of different language domains. A CLLD evaluation framework is proposed for system performance benchmarking. The framework includes standard document collections, evaluation metrics, and link assessment and evaluation tools. The evaluation methods described in this paper have been utilised to quantify the system performance at NTCIR-9 Crosslink task. It is shown that using the manual assessment for generating gold standard can deliver a more reliable evaluation result.

Cross-Lingual knowledge discovery : Chinese-to-English article linking in Wikipedia

In this paper we examine automated Chinese to English link discovery in Wikipedia and the effects of Chinese segmentation and Chinese to English translation on the hyperlink recommendation. Our experimental results show that the implemented link discovery framework can effectively recommend Chinese-to-English cross-lingual links. The techniques described here can assist bi-lingual users where a particular topic is not covered in Chinese, is not equally covered in both languages, or is biased in one language; as well as for language learning.

An evaluation framework for cross-lingual link discovery

Cross-Lingual Link Discovery (CLLD) is a new problem in Information Retrieval. The aim is to automatically identify meaningful and relevant hypertext links between documents in different languages. This is particularly helpful in knowledge discovery if a multi-lingual knowledge base is sparse in one language or another, or the topical coverage in each language is different; such is the case with Wikipedia. Techniques for identifying new and topically relevant cross-lingual links are a current topic of interest at NTCIR where the CrossLink task has been running since the 2011 NTCIR-9. This paper presents the evaluation framework for benchmarking algorithms for cross-lingual link discovery evaluated in the context of NTCIR-9. This framework includes topics, document collections, assessments, metrics, and a toolkit for pooling, assessment, and evaluation. The assessments are further divided into two separate sets: manual assessments performed by human assessors; and automatic assessments based on links extracted from Wikipedia itself. Using this framework we show that manual assessment is more robust than automatic assessment in the context of cross-lingual link discovery.

Overview of the NTCIR-10 cross-lingual link discovery task

This paper presents an overview of NTCIR-10 Cross-lingual Link Discovery (CrossLink-2) task. For the task, we continued using the evaluation framework developed for the NTCIR-9 CrossLink-1 task. Overall, recommended links were evaluated at two levels (file-to-file and anchor-to-file); and system performance was evaluated with metrics: LMAP, R-Prec and P@N.

Six Curses

This creative writing work was selected for publication in a bi-lingual anthology, published in China, as suitable to be culturally applicable to both Chinese and Australian social contexts. The poem raises six social/ethical issues and comments on them. It is based on research into Chinese traditional poetry that focuses on an image, and after each image this poem provides an ethical comment. It is based in the ethical hypothesis that moral evaluation of individual and social behaviour can not be achieved without ethical judgement which questions social norms. In particular, the poem questions the validity of fundamentalism – the belief in religious, scientific and moral absolutes. This is a key issue in contemporary research into the effect of religion on politics. It also draws on contemporary psychological theory, especially the concept of narcissism. The sociological basis of the work is in drawing parallels between eastern and western ethical issues, stressing similarity by inference. The imagery on which the poem is based selects objects such a single ‘stone’ that take on symbolic connotations common to both Australian and Chinese readers. This is innovative, since very little creative writing has been dome to address commonalities between Australian and Chinese ethical thinking, especially by adopting Chinese motifs.

Tip-over injuries in children under 5 years of age in Queensland 1999-2008

Furniture and appliance related injuries in children under 5 years of age accounts for an estimated 180 emergency presentations annually in Queensland. Injuries occur when children push or pull items over, climb and fall off furniture, or climb and tip the item over. Children under 2 years of age tend to injure themselves by pulling items over onto themselves Children over 2 years of age are more likely to be injured after climbing the item and either falling off or tipping the item over onto themselves. Tip over injuries (where the item falls over and injures the child) in children under 5 years of age account for an estimated 115 emergency presentations annually in Queensland. The item most commonly associated with a tip over injury is a television (with or without the cabinet) Prevention requires better design and selection of furniture with inherent stability coupled with mechanisms to install or fix less stable items

Towards improved speech recognition for resource poor languages

In recent times, the improved levels of accuracy obtained by Automatic Speech Recognition (ASR) technology has made it viable for use in a number of commercial products. Unfortunately, these types of applications are limited to only a few of the world’s languages, primarily because ASR development is reliant on the availability of large amounts of language specific resources. This motivates the need for techniques which reduce this language-specific, resource dependency. Ideally, these approaches should generalise across languages, thereby providing scope for rapid creation of ASR capabilities for resource poor languages. Cross Lingual ASR emerges as a means for addressing this need. Underpinning this approach is the observation that sound production is largely influenced by the physiological construction of the vocal tract, and accordingly, is human, and not language specific. As a result, a common inventory of sounds exists across languages; a property which is exploitable, as sounds from a resource poor, target language can be recognised using models trained on resource rich, source languages. One of the initial impediments to the commercial uptake of ASR technology was its fragility in more challenging environments, such as conversational telephone speech. Subsequent improvements in these environments has gained consumer confidence. Pragmatically, if cross lingual techniques are to considered a viable alternative when resources are limited, they need to perform under the same types of conditions. Accordingly, this thesis evaluates cross lingual techniques using two speech environments; clean read speech and conversational telephone speech. Languages used in evaluations are German, Mandarin, Japanese and Spanish. Results highlight that previously proposed approaches provide respectable results for simpler environments such as read speech, but degrade significantly when in the more taxing conversational environment. Two separate approaches for addressing this degradation are proposed. The first is based on deriving better target language lexical representation, in terms of the source language model set. The second, and ultimately more successful approach, focuses on improving the classification accuracy of context-dependent (CD) models, by catering for the adverse influence of languages specific phonotactic properties. Whilst the primary research goal in this thesis is directed towards improving cross lingual techniques, the catalyst for investigating its use was based on expressed interest from several organisations for an Indonesian ASR capability. In Indonesia alone, there are over 200 million speakers of some Malay variant, provides further impetus and commercial justification for speech related research on this language. Unfortunately, at the beginning of the candidature, limited research had been conducted on the Indonesian language in the field of speech science, and virtually no resources existed. This thesis details the investigative and development work dedicated towards obtaining an ASR system with a 10000 word recognition vocabulary for the Indonesian language.

Automatic spoken language identification utilizing acoustic and phonetic speech information

Automatic spoken Language Identi¯cation (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popularity of the Internet will amplify the need for the capabilities spoken language identi¯ca- tion systems provide. A prominent application arises in call centers dealing with speakers speaking di®erent languages. Another important application is to index or search huge speech data archives and corpora that contain multiple languages. The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology (NIST) Language Recognition Evaluation. Acoustic and phonetic speech information are targeted as the most suitable fea- tures for representing the characteristics of a language. To model the acoustic speech features a Gaussian Mixture Model based approach is employed. Pho- netic speech information is extracted using existing speech recognition technol- ogy. Various techniques to improve LID accuracy are also studied. One approach examined is the employment of Vocal Tract Length Normalization to reduce the speech variation caused by di®erent speakers. A linear data fusion technique is adopted to combine the various aspects of information extracted from speech. As a result of this research, a LID system was implemented and presented for evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.

An ecological footprint for an early learning centre : identifying opportunities for early childhood sustainability education through interdisciplinary research

In this study, engineers and educators worked together to adapt and apply the ecological footprint (EF) methodology to an early learning centre in Brisbane, Australia. Results were analysed to determine how environmental impact can be reduced at the study site and more generally across early childhood settings. It was found that food, transport and energy consumption had the largest impact on the centre’s overall footprint. In transport and energy, early childhood centres can reduce their impact through infrastructure and cultural change, in association with changed curriculum strategies. Building design, the type of energy purchased and appliance usage can all be modified to reduce the energy footprint. The transport footprint can be reduced through more families using active and public transport, which can be encouraged by providing information, support and facilities and appropriate siting of new centres. Introducing the concept of ecological footprint in early childhood education may be an effective way to educate children, staff and parents on the links between the food they eat, land usage and environmental impact. This study responds directly to the call in this journal for research focused on early childhood education and for more to be made of interdisciplinary research opportunities.

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.