Improving Search through A3C Reinforcement Learning based Conversational Agent

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.Comment: 17 pages, 7 figure

A lexical database tool for quantitative phonological research

A lexical database tool tailored for phonological research is described. Database fields include transcriptions, glosses and hyperlinks to speech files. Database queries are expressed using HTML forms, and these permit regular expression search on any combination of fields. Regular expressions are passed directly to a Perl CGI program, enabling the full flexibility of Perl extended regular expressions. The regular expression notation is extended to better support phonological searches, such as search for minimal pairs. Search results are presented in the form of HTML or LaTeX tables, where each cell is either a number (representing frequency) or a designated subset of the fields. Tables have up to four dimensions, with an elegant system for specifying which fragments of which fields should be used for the row/column labels. The tool offers several advantages over traditional methods of analysis: (i) it supports a quantitative method of doing phonological research; (ii) it gives universal access to the same set of informants; (iii) it enables other researchers to hear the original speech data without having to rely on published transcriptions; (iv) it makes the full power of regular expression search available, and search results are full multimedia documents; and (v) it enables the early refutation of false hypotheses, shortening the analysis-hypothesis-test loop. A life-size application to an African tone language (Dschang) is used for exemplification throughout the paper. The database contains 2200 records, each with approximately 15 fields. Running on a PC laptop with a stand-alone web server, the `Dschang HyperLexicon' has already been used extensively in phonological fieldwork and analysis in Cameroon.Comment: 7 pages, uses ipamacs.st

Similarity of Semantic Relations

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This paper introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) the patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM

Towards a Tool for Teaching Geometry to Children

An approach for a query-by-sketch system on qualitative shape information for image retrieval in databases is proposed and evaluated. The use of qualitative methods for shape description allows the gathering of semantic information from the sketches. The qualitative description and recognition of sketches are evaluated in order to verify that it is possible to use the proposed qualitative method for the development of a learning application for children

ODINet - Online Data Integration Network

Along with the expansion of Open Data and according to the latest EU directives for open access, the attention of public administration, research bodies and business is on web publishing of data in open format. However, a specialized search engine on the datasets, with similar role to that of Google for web pages, is not yet widespread. This article presents the Online Data Integration Network (ODINet) project, which aims to define a new technological framework for access to and online dissemination of structured and heterogeneous data through innovative methods of cataloging, searching and display of data on the web. In this article, we focus on the semantic component of our platform, emphasizing how we built and used ontologies. We further describe the Social Network Analysis (SNA) techniques we exploited to analyze it and to retrieve the required information. The testing phase of the project, that is still in progress, has already demonstrated the validity of the ODINet approach

Understanding User Behavioral Intention to Adopt a Search Engine that Promotes Sustainable Water Management

An increase in users’ online searches, the social concern for an efficient management of resources such as water, and the appearance of more and more digital platforms for sustainable purposes to conduct online searches lead us to reflect more on the users’ behavioral intention with respect to search engines that support sustainable projects like water management projects. Another issue to consider is the factors that determine the adoption of such search engines. In the present study, we aim to identify the factors that determine the intention to adopt a search engine, such as Lilo, that favors sustainable water management. To this end, a model based on the Theory of Planned Behavior (TPB) is proposed. The methodology used is the Structural Equation Modeling (SEM) analysis with the Analysis of Moment Structures (AMOS). The results demonstrate that individuals who intend to use a search engine are influenced by hedonic motivations, which drive their feeling of contentment with the search. Similarly, the success of search engines is found to be closely related to the ability a search engine grants to its users to generate a social or environmental impact, rather than users’ trust in what they do or in their results. However, according to our results, habit is also an important factor that has both a direct and an indirect impact on users’ behavioral intention to adopt different search engines

Testing the stability of “wisdom of crowds” judgments of search results over time and their similarity with the search engine rankings

PURPOSE: One of the under-explored aspects in the process of user information seeking behaviour is influence of time on relevance evaluation. It has been shown in previous studies that individual users might change their assessment of search results over time. It is also known that aggregated judgments of multiple individual users can lead to correct and reliable decisions; this phenomenon is known as the “wisdom of crowds”. The aim of this study is to examine whether aggregated judgments will be more stable and thus more reliable over time than individual user judgments. DESIGN/METHODS: In this study two simple measures are proposed to calculate the aggregated judgments of search results and compare their reliability and stability to individual user judgments. In addition, the aggregated “wisdom of crowds” judgments were used as a means to compare the differences between human assessments of search results and search engine’s rankings. A large-scale user study was conducted with 87 participants who evaluated two different queries and four diverse result sets twice, with an interval of two months. Two types of judgments were considered in this study: 1) relevance on a 4-point scale, and 2) ranking on a 10-point scale without ties. FINDINGS: It was found that aggregated judgments are much more stable than individual user judgments, yet they are quite different from search engine rankings. Practical implications: The proposed “wisdom of crowds” based approach provides a reliable reference point for the evaluation of search engines. This is also important for exploring the need of personalization and adapting search engine’s ranking over time to changes in users preferences. ORIGINALITY/VALUE: This is a first study that applies the notion of “wisdom of crowds” to examine the under-explored phenomenon in the literature of “change in time” in user evaluation of relevance

Automated generation of geometrically-precise and semantically-informed virtual geographic environnements populated with spatially-reasoning agents

La Géo-Simulation Multi-Agent (GSMA) est un paradigme de modélisation et de simulation de phénomènes dynamiques dans une variété de domaines d'applications tels que le domaine du transport, le domaine des télécommunications, le domaine environnemental, etc. La GSMA est utilisée pour étudier et analyser des phénomènes qui mettent en jeu un grand nombre d'acteurs simulés (implémentés par des agents) qui évoluent et interagissent avec une représentation explicite de l'espace qu'on appelle Environnement Géographique Virtuel (EGV). Afin de pouvoir interagir avec son environnement géographique qui peut être dynamique, complexe et étendu (à grande échelle), un agent doit d'abord disposer d'une représentation détaillée de ce dernier. Les EGV classiques se limitent généralement à une représentation géométrique du monde réel laissant de côté les informations topologiques et sémantiques qui le caractérisent. Ceci a pour conséquence d'une part de produire des simulations multi-agents non plausibles, et, d'autre part, de réduire les capacités de raisonnement spatial des agents situés. La planification de chemin est un exemple typique de raisonnement spatial dont un agent pourrait avoir besoin dans une GSMA. Les approches classiques de planification de chemin se limitent à calculer un chemin qui lie deux positions situées dans l'espace et qui soit sans obstacle. Ces approches ne prennent pas en compte les caractéristiques de l'environnement (topologiques et sémantiques), ni celles des agents (types et capacités). Les agents situés ne possèdent donc pas de moyens leur permettant d'acquérir les connaissances nécessaires sur l'environnement virtuel pour pouvoir prendre une décision spatiale informée. Pour répondre à ces limites, nous proposons une nouvelle approche pour générer automatiquement des Environnements Géographiques Virtuels Informés (EGVI) en utilisant les données fournies par les Systèmes d'Information Géographique (SIG) enrichies par des informations sémantiques pour produire des GSMA précises et plus réalistes. De plus, nous présentons un algorithme de planification hiérarchique de chemin qui tire avantage de la description enrichie et optimisée de l'EGVI pour fournir aux agents un chemin qui tient compte à la fois des caractéristiques de leur environnement virtuel et de leurs types et capacités. Finalement, nous proposons une approche pour la gestion des connaissances sur l'environnement virtuel qui vise à supporter la prise de décision informée et le raisonnement spatial des agents situés