NHS Wales user needs study including knowledgebase tools report. Report for Informing Healthcare Strategy implementation programme.

Thomas, R., Spink, S., Durbin, J. & Urquhart, C. (2005). NHS Wales user needs study including knowledgebase tools report. Report for Informing Healthcare Strategy implementation programme. Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: Informing Healthcare, NHS Wales

Query Suggestions for Textual Problem Solution Repositories

Textual problem-solution repositories are available today in
various forms, most commonly as problem-solution pairs from community
question answering systems. Modern search engines that operate on
the web can suggest possible completions in real-time for users as they
type in queries. We study the problem of generating intelligent query
suggestions for users of customized search systems that enable querying
over problem-solution repositories. Due to the small scale and specialized
nature of such systems, we often do not have the luxury of depending on
query logs for finding query suggestions. We propose a retrieval model
for generating query suggestions for search on a set of problem solution
pairs. We harness the problem solution partition inherent in such
repositories to improve upon traditional query suggestion mechanisms
designed for systems that search over general textual corpora. We evaluate
our technique over real problem-solution datasets and illustrate that
our technique provides large and statistically significant

Comparing segmentation strategies for efficient video passage retrieval

We compare the effect of different text segmentation strategies on speech based passage retrieval of video. Passage retrieval has mainly been studied to improve document retrieval and to enable question answering. In these domains best results were obtained using passages defined by the paragraph structure of the source documents or by using arbitrary overlapping passages. For the retrieval of relevant passages in a video, using speech transcripts, no author defined segmentation is available. We compare retrieval results from 4 different types of segments based on the speech channel of the video: fixed length segments, a sliding window, semantically coherent segments and prosodic segments. We evaluated the methods on the corpus of the MediaEval 2011 Rich Speech Retrieval task. Our main conclusion is that the retrieval results highly depend on the right choice for the segment length. However, results using the segmentation into semantically coherent parts depend much less on the segment length. Especially, the quality of fixed length and sliding window segmentation drops fast when the segment length increases, while quality of the semantically coherent segments is much more stable. Thus, if coherent segments are defined, longer segments can be used and consequently less segments have to be considered at retrieval time.

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs).

Arquitetura para aquisição de respostas a perguntas frequentes

Dissertação de natureza científica realizada para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Cooperative learning and elaborative interrogation: effects on children's learning

This experimental study examined the effects of cooperative learning and a question-answering strategy called elaborative interrogation ("Why is this fact true?") on the learning of factual information about familiar animals. Retention gains were compared across four study conditions: elaborative-interrogation-plus-cooperative learning, cooperative-learning, elaborative-interrogation, and reading-control. Sixth-grade students (n=68) were randomly assigned to the four conditions. All participants were given initial training and practice in cooperative learning procedures via three 45-minute sessions. After studying 36 facts about six animals, students' retention gains were measured via immediate free recall, immediate matched association, 30-day, and GO-day matched association tests. A priori comparisons were made to analyze the data. For immediate free recall and immediate matched association, significant differences were found between students in the three experimental conditions versus those in the control condition. Elaborative-interrogation and elaborativeinterrogation- plus-cooperative-learning also promoted longterm retention (measured via 30-day matched association) of the material relative to repetitive reading with elaborative-interrogation promoting the most durable gains (measured via GO-day matched association). The relationship between the types of elaborative responses and probability of subsequent retention was also examined. Even when students were unable to provide adequate answers to the why questions, learning was facilitated more so than repetitive reading. In general, generation of adequate elaborations was associated with greater probability of recall than was provision of inadequate answers. The findings of the study demonstrate that cooperative learning and the use of elaborative interrogation, both individually and collaboratively, are effective classroom procedures for facilitating children's learning of new information.

The Impact of a Text-Reader Support Program on Intermediate Students' Reading Comprehension

This study examined the efficacy of providing four Grade 7 and 8 students with reading difficulties with explicit instruction in the use of reading comprehension strategies while using text-reader software. Specifically, the study explored participants' combined use of a text-reader and question-answering comprehension strategy during a 6-week instructional program. Using a qualitative case study methodology approach, participants' experiences using text-reader software, with the presence of explicit instruction in evidence-based reading comprehension strategies, were examined. The study involved three phases: (a) the first phase consisted of individual interviews with the participants and their parents; (b) the second phase consisted of a nine session course; and (c) the third phase consisted of individual exit interviews and a focus group discussion. After the data collection phases were completed, data were analyzed and coded for emerging themes, with-quantitativ,e measures of participants' reading performance used as descriptive data. The data suggested that assistive technology can serve as an instructional "hook", motivating students to engage actively in the reading processes, especially when accompanied by explicit strategy instruction. Participants' experiences also reflected development of strategy use and use of text-reader software and the importance of social interactions in developing reading comprehension skills. The findings of this study support the view that the integration of instruction using evidence-based practices are important and vital components in the inclusion oftext-reader software as part of students' educational programming. Also, the findings from this study can be extended to develop in-class programming for students using text-reader software.

Fundo de investimento imobiliário: metodologia para subsidiar o investidor a formar uma carteira eficiente

O presente trabalho tem por objetivo subsidiar o investidor de Fundos de Investimento Imobiliário na escolha de uma carteira de aplicação de FIIs, visando obter performance igual ou superior ao índice de referência do setor (IFIX). Tal subsídio é constituído, inicialmente, por uma metodologia que considera que o conceito de Carteira Eficiente (Risco/Retorno) preconizada por Markowitz pode trabalhar em conjunto com a dimensão do conceito das Finanças Comportamentais, liderada por Daniel Kahneman, constituindo as bases de orientação do investidor. Acrescentamos o caminho metodológico com as indicações, sugeridas por Bazerman e Moore, no processo de tomada de decisão, que reduza os efeitos de heurísticas e vieses.

The Semantics and Typology of Yes/No Particles (A Cross-Linguistic Study)

Taking the three basic systems of Yes/No particles the group looked at the relative deep and surface structures, and asked what types of systems are present in the Georgian, Polish and Armenian languages. The choice of languages was of particular interest as the Caucasian and Indo-European languages usually have different question-answering systems, but Georgian (Caucasian) and Polish (Indo-European) in fact share the same system. The Armenian language is Indo-European, but the country is situated in the southern Caucasus, on Georgia's southern border, making it worth analysing Armenian in comparison with Georgian (from the point of view of language interference) and with Polish (as two relative languages). The group identified two different deep structures, tracing the occurrence of these in different languages, and showed that one is more natural in the majority of languages. They found no correspondence between relative languages and their question-answer systems and demonstrated that languages in the same typological class may show different systems, as with Georgian and the North Caucasian languages. It became clear that Georgian, Armenian and Polish all have an agree/disagree question-answering system defined by the same deep structure. From this they conclude that the lingual mentalities of Georgians, Armenians and Poles are more oriented to the communicative act. At the same time the Yes/No system, in which a positive particle stands for a positive answer and a negative particle for a negative answer, also functions in these languages, indicating that the second deep structure identified also functions alongside the first.

Quantity and degree assessment in an RDF.based semantic language

This paper introduces a semantic language developed with the objective to be used in a semantic analyzer based on linguistic and world knowledge. Linguistic knowledge is provided by a Combinatorial Dictionary and several sets of rules. Extra-linguistic information is stored in an Ontology. The meaning of the text is represented by means of a series of RDF-type triples of the form predicate (subject, object). Semantic analyzer is one of the options of the multifunctional ETAP-3 linguistic processor. The analyzer can be used for Information Extraction and Question Answering. We describe semantic representation of expressions that provide an assessment of the number of objects involved and/or give a quantitative evaluation of different types of attributes. We focus on the following aspects: 1) parametric and non-parametric attributes; 2) gradable and non-gradable attributes; 3) ontological representation of different classes of attributes; 4) absolute and relative quantitative assessment; 5) punctual and interval quantitative assessment; 6) intervals with precise and fuzzy boundaries

A knowledge-based textual entailment approach applied to the QA answer validation at CLEF 2006

The Answer Validation Exercise (AVE) is a pilot track within the Cross-Language Evaluation Forum (CLEF) 2006. The AVE competition provides an evaluation frame- work for answer validations in Question Answering (QA). In our participation in AVE, we propose a system that has been initially used for other task as Recognising Textual Entailment (RTE). The aim of our participation is to evaluate the improvement our system brings to QA. Moreover, due to the fact that these two task (AVE and RTE) have the same main idea, which is to find semantic implications between two fragments of text, our system has been able to be directly applied to the AVE competition. Our system is based on the representation of the texts by means of logic forms and the computation of semantic comparison between them. This comparison is carried out using two different approaches. The first one managed by a deeper study of the Word- Net relations, and the second uses the measure defined by Lin in order to compute the semantic similarity between the logic form predicates. Moreover, we have also designed a voting strategy between our system and the MLEnt system, also presented by the University of Alicante, with the aim of obtaining a joint execution of the two systems developed at the University of Alicante. Although the results obtained have not been very high, we consider that they are quite promising and this supports the fact that there is still a lot of work on researching in any kind of textual entailment.

Creación automática de sistemas de búsqueda de respuestas en dominios restringidos

Los sistemas de búsqueda de respuestas (BR) se pueden considerar como potenciales sucesores de los buscadores tradicionales de información en la Web. Para que sean precisos deben adaptarse a dominios concretos mediante el uso de recursos semánticos adecuados. La adaptación no es una tarea trivial, ya que deben integrarse e incorporarse a sistemas de BR existentes varios recursos heterogéneos relacionados con un dominio restringido. Se presenta la herramienta Maraqa, cuya novedad radica en el uso de técnicas de ingeniería del software, como el desarrollo dirigido por modelos, para automatizar dicho proceso de adaptación a dominios restringidos. Se ha evaluado Maraqa mediante una serie de experimentos (sobre el dominio agrícola) que demuestran su viabilidad, mejorando en un 29,5% la precisión del sistema adaptado.

Aplicaciones de procesamiento de lenguaje natural

El campo de procesamiento de lenguaje natural (PLN), ha tenido un gran crecimiento en los últimos años; sus áreas de investigación incluyen: recuperación y extracción de información, minería de datos, traducción automática, sistemas de búsquedas de respuestas, generación de resúmenes automáticos, análisis de sentimientos, entre otras. En este artículo se presentan conceptos y algunas herramientas con el fin de contribuir al entendimiento del procesamiento de texto con técnicas de PLN, con el propósito de extraer información relevante que pueda ser usada en un gran rango de aplicaciones. Se pueden desarrollar clasificadores automáticos que permitan categorizar documentos y recomendar etiquetas; estos clasificadores deben ser independientes de la plataforma, fácilmente personalizables para poder ser integrados en diferentes proyectos y que sean capaces de aprender a partir de ejemplos. En el presente artículo se introducen estos algoritmos de clasificación, se analizan algunas herramientas de código abierto disponibles actualmente para llevar a cabo estas tareas y se comparan diversas implementaciones utilizando la métrica F en la evaluación de los clasificadores.

Application of Text Summarization techniques to the Geographical Information Retrieval task

Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field.

An authoring tool for decision support systems in context questions of ecological knowledge

Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.