67 research outputs found
Extraction of Process Models from Business Process Descriptions
The purpose of my work is to design a method to transform a textual process description (in English) into a business process model. This is of practical relevance, since process models are often designed by business analysts starting from textual documentation. The method to be designed aims at automating the text-to-diagram conversion phase as much as possible.
Natural languages are known to be highly complex and ambiguous. Accordingly, for this project we will approach the problem using a best-effort approach, meaning that the method is not intended to work always. Instead, the proposed approach will be able to detect certain sentence structures and extract actors, actions and objects/artifacts from them. Coordinating and subordinating conjunctions, as well as punctuation and other markers, will be used to identify sequencing, parallelism, conditional branching and repetition. The output of the method will be a block-structured process model.
The method is being implemented in Java based on open-source Natural-Language Processing (NLP) libraries. Specifically, Part-of-Speech (POS) tagging is performed using the Stanford parser and according to the POS tags, corresponding process entities are identified using Tregex and Tsurgeon. The current implementation is already able to identify actors, actions/tasks and artifacts from sentences that abide to certain common structures. Additionally the implementation is able to correctly interpret passive voice construction, avoid articles, parenthesis and other complex structures for the purpose of extracting essential information about the process
Conversational Exploratory Search via Interactive Storytelling
Conversational interfaces are likely to become more efficient, intuitive and
engaging way for human-computer interaction than today's text or touch-based
interfaces. Current research efforts concerning conversational interfaces focus
primarily on question answering functionality, thereby neglecting support for
search activities beyond targeted information lookup. Users engage in
exploratory search when they are unfamiliar with the domain of their goal,
unsure about the ways to achieve their goals, or unsure about their goals in
the first place. Exploratory search is often supported by approaches from
information visualization. However, such approaches cannot be directly
translated to the setting of conversational search.
In this paper we investigate the affordances of interactive storytelling as a
tool to enable exploratory search within the framework of a conversational
interface. Interactive storytelling provides a way to navigate a document
collection in the pace and order a user prefers. In our vision, interactive
storytelling is to be coupled with a dialogue-based system that provides verbal
explanations and responsive design. We discuss challenges and sketch the
research agenda required to put this vision into life.Comment: Accepted at ICTIR'17 Workshop on Search-Oriented Conversational AI
(SCAI 2017
An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues
The ability to engage in mixed-initiative interaction is one of the core
requirements for a conversational search system. How to achieve this is poorly
understood. We propose a set of unsupervised metrics, termed ConversationShape,
that highlights the role each of the conversation participants plays by
comparing the distribution of vocabulary and utterance types. Using
ConversationShape as a lens, we take a closer look at several conversational
search datasets and compare them with other dialogue datasets to better
understand the types of dialogue interaction they represent, either driven by
the information seeker or the assistant. We discover that deviations from the
ConversationShape of a human-human dialogue of the same type is predictive of
the quality of a human-machine dialogue.Comment: SIGIR 2020 short conference pape
Innovative educational technologies as a way of higher education enhancement
The article examines the interconnected functions of the innovative activity of the teacher, which contribute to the improvement of higher education. The purpose of the article is to consider innovative educational technologies as a means of improving higher education and to prove their impact on the training of a modern competitive specialist. The methodology shows the connection of the main methodological approaches of professional training of a modern competitive specialist with the help of innovative educational technologies as a means of improving higher education. The levels of training of the future specialist with innovative educational means are highlighted. The classification of innovative educational technologies was carried out. The importance of hybrid courses, which include a form of combination of distance learning and face-to-face learning, is shown. The features are identified and the necessity of innovative educational technologies for the improvement of higher education is shown. Innovative educational technologies have advantages in higher education. The criteria of innovations in the educational process are singled out. The features of innovative training are listed, which are of great importance for the improvement of higher education and, as a result, obtaining a competitive specialist
Enriching iTunes App Store Categories via Topic Modeling
Mobile application development is an emerging lucrative and fast growing market. With the steady growth of the number of apps in the repositories the providers will inevitably face the need to fine-grain the existing hierarchy of categories used to organize the apps. In this paper we present a method to bootstrap the categorization process via topic modeling. We apply Latent Dirichlet Allocation (LDA) to the textual descriptions of iTunes apps in order to identify recurrent topics in the collection. We evaluate and discuss the results obtained from training the model on a set of almost 600,000 English-language app descriptions. Our results demonstrate that automated categorization via LDA-based topic modeling is a promising approach, that can help to structure, analyze and manage the content of app repositories. The topics produced complement the original iTunes categories, concretize and extend them by providing insights into the underlying category content
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