Analyzing Hyperonyms of Stack Overflow Posts

Communication among people is often a challenging task due to the different interpretations of the terms they use. The way people interpret the terms highly depends on the semantic context, where the notions were acquired. The different contexts provide somewhat distinct meanings to the terms used. In software development and integration, requirements engineering and customer support are primarily affected by the difficulties stemming from communication obstacles. The necessary information is often inadequately forwarded to developers resulting in poorly specified software requirements or misinterpreted user feedback. The communication difficulties mentioned can be solved by clarifying the meanings of the concepts used. Semantic networks built on different contexts are suitable tools for this purpose. This paper presents a formal description of the semantic network and the semantic space needed for the algorithmic treatment of the concepts. It provides a model for extracting hyperonymy and hyponymy relations from text corpora created in specific semantic domains. The model was applied on a corpus acquired from Stack Overflow containing conversations among the software developers to solve programming issues

Analyzing Hyperonyms of Stack Overflow Posts

Communication among people is often a challenging task due to the different interpretations of the terms they use. The way people interpret the terms highly depends on the semantic context, where the notions were acquired. The different contexts provide somewhat distinct meanings to the terms used. In software development and integration, requirements engineering and customer support are primarily affected by the difficulties stemming from communication obstacles. The necessary information is often inadequately forwarded to developers resulting in poorly specified software requirements or misinterpreted user feedback. The communication difficulties mentioned can be solved by clarifying the meanings of the concepts used. Semantic networks built on different contexts are suitable tools for this purpose. This paper presents a formal description of the semantic network and the semantic space needed for the algorithmic treatment of the concepts. It provides a model for extracting hyperonymy and hyponymy relations from text corpora created in specific semantic domains. The model was applied on a corpus acquired from Stack Overflow containing conversations among the software developers to solve programming issues

Aggregated search: a new information retrieval paradigm

International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search

Conceptual Representations for Computational Concept Creation

Computational creativity seeks to understand computational mechanisms that can be characterized as creative. The creation of new concepts is a central challenge for any creative system. In this article, we outline different approaches to computational concept creation and then review conceptual representations relevant to concept creation, and therefore to computational creativity. The conceptual representations are organized in accordance with two important perspectives on the distinctions between them. One distinction is between symbolic, spatial and connectionist representations. The other is between descriptive and procedural representations. Additionally, conceptual representations used in particular creative domains, such as language, music, image and emotion, are reviewed separately. For every representation reviewed, we cover the inference it affords, the computational means of building it, and its application in concept creation.Peer reviewe

A framework for structuring prerequisite relations between concepts in educational textbooks

In our age we are experiencing an increasing availability of digital educational resources and self-regulated learning. In this scenario, the development of automatic strategies for organizing the knowledge embodied in educational resources has a tremendous potential for building personalized learning paths and applications such as intelligent textbooks and recommender systems of learning materials. To this aim, a straightforward approach consists in enriching the educational materials with a concept graph, i.a. a knowledge structure where key concepts of the subject matter are represented as nodes and prerequisite dependencies among such concepts are also explicitly represented. This thesis focuses therefore on prerequisite relations in textbooks and it has two main research goals. The first goal is to define a methodology for systematically annotating prerequisite relations in textbooks, which is functional for analysing the prerequisite phenomenon and for evaluating and training automatic methods of extraction. The second goal concerns the automatic extraction of prerequisite relations from textbooks. These two research goals will guide towards the design of PRET, i.e. a comprehensive framework for supporting researchers involved in this research issue. The framework described in the present thesis allows indeed researchers to conduct the following tasks: 1) manual annotation of educational texts, in order to create datasets to be used for machine learning algorithms or for evaluation as gold standards; 2) annotation analysis, for investigating inter-annotator agreement, graph metrics and in-context linguistic features; 3) data visualization, for visually exploring datasets and gaining insights of the problem that may lead to improve algorithms; 4) automatic extraction of prerequisite relations. As for the automatic extraction, we developed a method that is based on burst analysis of concepts in the textbook and we used the gold dataset with PR annotation for its evaluation, comparing the method with other metrics for PR extraction