Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).

Food for Thought: Emergence of Food-Based Historical Museum Walking Tours

Thesis (Master's)--University of Washington, 2016-06

A semantic modelling approach to knowledge based statistical software

The topic of this thesis is the development of knowledge based statistical software. The shortcomings of conventional statistical packages are discussed to illustrate the need to develop software which is able to exhibit a greater degree of statistical expertise, thereby reducing the misuse of statistical methods by those not well versed in the art of statistical analysis. Some of the issues involved in the development of knowledge based software are presented and a review is given of some of the systems that have been developed so far. The majority of these have moved away from conventional architectures by adopting what can be termed an expert systems approach. The thesis then proposes an approach which is based upon the concept of semantic modelling. By representing some of the semantic meaning of data, it is conceived that a system could examine a request to apply a statistical technique and check if the use of the chosen technique was semantically sound, i.e. will the results obtained be meaningful. Current systems, in contrast, can only perform what can be considered as syntactic checks. The prototype system that has been implemented to explore the feasibility of such an approach is presented, the system has been designed as an enhanced variant of a conventional style statistical package. This involved developing a semantic data model to represent some of the statistically relevant knowledge about data and identifying sets of requirements that should be met for the application of the statistical techniques to be valid. Those areas of statistics covered in the prototype are measures of association and tests of location.

PSS as a service based strategy for western manufacturers

A product-service system (PSS) is a subtle blend of products and services that are offered as an integrated solution to customers. Much existing work on PSS has originated from Scandinavia and has been motivated by a sustainability agenda. Although valuable, this form has limited appeal to western manufacturers. However, by expanding the concepts of PSS to also embrace leading thinking on large scale complex service systems and informated products and services, it is possible to put forward the basis of a service business model that offers the means to differentiate from competitors who simply offer lower priced products. This paper aims to build this case. It reports the state-of-the-art of PSS, defines the concept, reports on its origin and features, discusses examples of applications, and finally proposes a research strategy for future work on this topic.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Do stressful times call for team-based working? A study of psychiatric nurses in UK mental healthcase

Background: Team-based working is now an inherent part of effective health care delivery. Previous research has identified that team working is associated with positive mental health and well-being outcomes for individuals operating in an effective team environment. This is a particularly important topic in the health services context, although little empirical attention has been paid to mental-health services. Psychiatric nurses work on a day-to-day basis with a particularly stressful and demanding client group in an environment which is characterised by high demands, uncertainty, and limited resources. This paper specifically focuses on psychiatric nurses working in National Health Service (NHS) and casts some light on the ways in which effective team-based working can help to alleviate a number of occupational stressors and strains. Method: A questionnaire method (2005 NHS Staff Survey) was employed to collect data from 6655 psychiatric nurses from 64 different NHS Trusts. The hypotheses were concerned with four overall measures from the survey; effective team working, occupational stress, work pressure and social support. Hypothesis 1 stated that effective team working will have a significant negative relationship with occupational stress and work pressure. Further, Hypothesis 2 stated that social support from supervisors and co-workers will moderate this relationship. Findings: Data was treated with a series of regression analyses. For Hypothesis 1, working in a real team did have main effects on work pressure and accounted for 1.6 per cent of the variance. Using the Nagelkerke R square value, working in a real team also had main effects on occupational stress an accounted for approximately 2.8 per cent of the variance. Further, the Exp (B) value of 0.662 suggests that the odds of suffering from occupational stress are cut by 33.8 per cent when a psychiatric nurse works in a real team. Results failed to provide support for Hypothesis 2. The analysis then went on to adopt a unique approach for assessing the extent of real team-based working, distinguishing between real teams, and a number of pseudo team typologies, as well as the absence of teamwork all together. As was hypothesised, results demonstrated that psychiatric nurses working in real teams (ones with clear objectives, where-by team members work closely with one another to achieve team objectives and meet regularly to discuss team effectiveness and how it can be improved) experienced the lowest levels of stress and work pressure of the sample. However, contrary to prediction, results indicated that psychiatric nurses working in any type of pseudo team actually experienced significantly higher levels of stress and work pressure than those who did not report as working in a team at all. Discussion: These findings have serious implications for NHS Mental Health Trusts, which may not be implementing, structuring and managing their nursing teams adequately. Indeed, results suggest that poorly-structured team work may actually facilitate stress and pressure in the workplace. Conversely, well-structured real teams serve to reduce stress and work pressure, which in turn not only enhances the working lives and well-being of psychiatric nurses, but also greatly improves the service that the NHS provides to its users.

Stressful times call for team-based measures:psychiatric nursing in the UK National Health Service

Background: Team-based working is now an inherent part of effective health care delivery. Previous research has identified that team working is associated with positive mental health and well-being outcomes for individuals operating in an effective team environment. This is a particularly important topic in the health services context, although little empirical attention has been paid to mental-health services. Psychiatric nurses work on a day-to-day basis with a particularly stressful and demanding client group in an environment which is characterised by high demands, uncertainty, and limited resources. This paper specifically focuses on psychiatric nurses working in National Health Service (NHS) and casts some light on the ways in which effective team-based working can help to alleviate a number of occupational stressors and strains. Method: A questionnaire method (2005 NHS Staff Survey) was employed to collect data from 6655 psychiatric nurses from 64 different NHS Trusts. The hypotheses were concerned with four overall measures from the survey; effective team working, occupational stress, work pressure and social support. Hypothesis 1 stated that effective team working will have a significant negative relationship with occupational stress and work pressure. Further, Hypothesis 2 stated that social support from supervisors and co-workers will moderate this relationship. Findings: Data was treated with a series of regression analyses. For Hypothesis 1, working in a real team did have main effects on work pressure and accounted for 1.6 per cent of the variance. Using the Nagelkerke R square value, working in a real team also had main effects on occupational stress an accounted for approximately 2.8 per cent of the variance. Further, the Exp (B) value of 0.662 suggests that the odds of suffering from occupational stress are cut by 33.8 per cent when a psychiatric nurse works in a real team. Results failed to provide support for Hypothesis 2. The analysis then went on to adopt a unique approach for assessing the extent of real team-based working, distinguishing between real teams, and a number of pseudo team typologies, as well as the absence of teamwork all together. As was hypothesised, results demonstrated that psychiatric nurses working in real teams (ones with clear objectives, where-by team members work closely with one another to achieve team objectives and meet regularly to discuss team effectiveness and how it can be improved) experienced the lowest levels of stress and work pressure of the sample. However, contrary to prediction, results indicated that psychiatric nurses working in any type of pseudo team actually experienced significantly higher levels of stress and work pressure than those who did not report as working in a team at all. Discussion: These findings have serious implications for NHS Mental Health Trusts, which may not be implementing, structuring and managing their nursing teams adequately. Indeed, results suggest that poorly-structured team work may actually facilitate stress and pressure in the workplace. Conversely, well-structured real teams serve to reduce stress and work pressure, which in turn not only enhances the working lives and well-being of psychiatric nurses, but also greatly improves the service that the NHS provides to its users.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

Incorporating social role theory into topic models for social media content analysis

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.

A framework for automated construction of resource space based on background knowledge

Resource Space Model is a kind of data model which can effectively and flexibly manage the digital resources in cyber-physical system from multidimensional and hierarchical perspectives. This paper focuses on constructing resource space automatically. We propose a framework that organizes a set of digital resources according to different semantic dimensions combining human background knowledge in WordNet and Wikipedia. The construction process includes four steps: extracting candidate keywords, building semantic graphs, detecting semantic communities and generating resource space. An unsupervised statistical language topic model (i.e., Latent Dirichlet Allocation) is applied to extract candidate keywords of the facets. To better interpret meanings of the facets found by LDA, we map the keywords to Wikipedia concepts, calculate word relatedness using WordNet's noun synsets and construct corresponding semantic graphs. Moreover, semantic communities are identified by GN algorithm. After extracting candidate axes based on Wikipedia concept hierarchy, the final axes of resource space are sorted and picked out through three different ranking strategies. The experimental results demonstrate that the proposed framework can organize resources automatically and effectively.©2013 Published by Elsevier Ltd. All rights reserved.

From art for arts sake to art as means of knowing:a rationale for advancing arts-based methods in research, practice and pedagogy

This paper advances a philosophically informed rationale for the broader, reflexive and practical application of arts-based methods to benefit research, practice and pedagogy. It addresses the complexity and diversity of learning and knowing, foregrounding a cohabitative position and recognition of a plurality of research approaches, tailored and responsive to context. Appreciation of art and aesthetic experience is situated in the everyday, underpinned by multi-layered exemplars of pragmatic visual-arts narrative inquiry undertaken in the third, creative and communications sectors. Discussion considers semi-guided use of arts-based methods as a conduit for topic engagement, reflection and intersubjective agreement; alongside observation and interpretation of organically employed approaches used by participants within daily norms. Techniques span handcrafted (drawing), digital (photography), hybrid (cartooning), performance dimensions (improvised installations) and music (metaphor and structure). The process of creation, the artefact/outcome produced and experiences of consummation are all significant, with specific reflexivity impacts. Exploring methodology and epistemology, both the "doing" and its interpretation are explicated to inform method selection, replication, utility, evaluation and development of cross-media skills literacy. Approaches are found engaging, accessible and empowering, with nuanced capabilities to alter relationships with phenomena, experiences and people. By building a discursive space that reduces barriers; emancipation, interaction, polyphony, letting-go and the progressive unfolding of thoughts are supported, benefiting ways of knowing, narrative (re)construction, sensory perception and capacities to act. This can also present underexplored researcher risks in respect to emotion work, self-disclosure, identity and agenda. The paper therefore elucidates complex, intricate relationships between form and content, the represented and the representation or performance, researcher and participant, and the self and other. This benefits understanding of phenomena including personal experience, sensitive issues, empowerment, identity, transition and liminality. Observations are relevant to qualitative and mixed methods researchers and a multidisciplinary audience, with explicit identification of challenges, opportunities and implications.

A Computer-Based Strategy for Practical Skills Teaching

An adaptive learning technology embedded in e-learning environments ensures choice of the structure, content, and activities for each individual learner according to the teaching team’s domain and didactic knowledge and skills. In this paper a computer-based scenario for application of an adaptive navigation technology is proposed and demonstrated on an example course topic.

Topic Segmentation: How Much Can We Do by Counting Words and Sequences of Words

In this paper, we present an innovative topic segmentation system based on a new informative similarity measure that takes into account word co-occurrence in order to avoid the accessibility to existing linguistic resources such as electronic dictionaries or lexico-semantic databases such as thesauri or ontology. Topic segmentation is the task of breaking documents into topically coherent multi-paragraph subparts. Topic segmentation has extensively been used in information retrieval and text summarization. In particular, our architecture proposes a language-independent topic segmentation system that solves three main problems evidenced by previous research: systems based uniquely on lexical repetition that show reliability problems, systems based on lexical cohesion using existing linguistic resources that are usually available only for dominating languages and as a consequence do not apply to less favored languages and finally systems that need previously existing harvesting training data. For that purpose, we only use statistics on words and sequences of words based on a set of texts. This solution provides a flexible solution that may narrow the gap between dominating languages and less favored languages thus allowing equivalent access to information.

A Statistical Approach for Multilingual Document Clustering and Topic Extraction from Clusters

2000 Mathematics Subject Classification: 62H30

An analysis of the labor market situation of vocational students based on national and international data

The article focuses on the labour market situation and opportunities of the Hungarian vocational students. After briefly placing the topic in an international context, the study introduces the findings of the Hungarian empirical researches. Due to the differences between the various national education systems, it is not easy to make international comparisons; therefore I chose former socialist countries with characteristics similar to those of Hungary. When comparing the relevant data, it became clear that obtaining a diploma provides more advantages in Hungary. Hungarian researches suggest that vocational schools mostly attract students with poor competence test scores at the end of primary school. Also a significant proportion of these students are disadvantaged. Vocational students are the most likely to drop out of the system and their return to the school later is sporadic at best. Although a completed VET improves their employment conditions and prospects, many of the graduates will leave their profession or do unskilled labour. Their labour income varies greatly depending on their type of trade and experience gained.