Query-Based Summarization using Rhetorical Structure Theory

Research on Question Answering is focused mainly on classifying the question type and finding the answer. Presenting the answer in a way that suits the user’s needs has received little attention. This paper shows how existing question answering systems—which aim at finding precise answers to questions—can be improved by exploiting summarization techniques to extract more than just the answer from the document in which the answer resides. This is done using a graph search algorithm which searches for relevant sentences in the discourse structure, which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a graph representation of a text document. The output is an extensive answer, which not only answers the question, but also gives the user an opportunity to assess the accuracy of the answer (is this what I am looking for?), and to find additional information that is related to the question, and which may satisfy an information need. This has been implemented in a working multimodal question answering system where it operates with two independently developed question answering modules

Power of Interviewer in `Kick Andy` on Metro TV

Penelitian ini berjudul “Power of Interviewer in Kick Andy on Metro TV”. Penelitian ini bertujuan untuk menggambarkan kekuasaan pewawancara yang digunakan Andy F Noya terhadap bintang tamu dalam talk show Kick Andy. Bentuk penelitian yang digunakan adalah deskriptif kualitatif. Dalam penelitian ini digunakan data rekaman sebagai alat koleksi data dengan menggunakan 2 data sampel percakapan antara pewawancara (IR) dan narasumber (IE), yakni episode “Reformasi Ala Amin Rais” dan “More Heroes Big Action”. Penelitian inimenggunakan beberapa pendekatan meliputi; discourse, power, cooperative and politeness principle dan conversation analysis. Hasil dari penelitian ini menunjukkan bahwa IR memiliki kekuasaan terhadap IE. Terdapat bentuk-bentuk kekuasaan yang dimiliki IR dalam wawancara; (1) IR berkuasa untuk mengendalikan jalannya wawancara, termasuk mengendalikan IE, (2) IR lebih menerapkan prinsip kooperatif Grice daripada IE, (3) IR berkuasa untuk membuat rangkaian pertanyaan terhadap IE, (4) IR sedikit menerapkan prinsipkesopanan Leech dibandingkan IE, (5) IR bertanya dalam bentuk single-word, single-phrase, dan single-clauseturn, (6) Berdasarkan alokasi giliran bicara, kekuasaan IR meliputi, IR mengalokasikan diri sendiri dan mitra tutur (IE) yang dikehendaki bahkan IR juga berkuasa untuk menerima, mengabaikan, serta untuk tidak menjawab pertanyaan dari IE, (7) IR berkuasa untuk membuka dan menutup sebuah percakapan serta membentuk sekuen percakapan yang IR inginkan, (8) IR melakukan tindakan interupsi kooperatif bentuk klarifikasi daninterupsi kompetitif

Follow-up question handling in the IMIX and Ritel systems: A comparative study

One of the basic topics of question answering (QA) dialogue systems is how follow-up questions should be interpreted by a QA system. In this paper, we shall discuss our experience with the IMIX and Ritel systems, for both of which a follow-up question handling scheme has been developed, and corpora have been collected. These two systems are each other's opposites in many respects: IMIX is multimodal, non-factoid, black-box QA, while Ritel is speech, factoid, keyword-based QA. Nevertheless, we will show that they are quite comparable, and that it is fruitful to examine the similarities and differences. We shall look at how the systems are composed, and how real, non-expert, users interact with the systems. We shall also provide comparisons with systems from the literature where possible, and indicate where open issues lie and in what areas existing systems may be improved. We conclude that most systems have a common architecture with a set of common subtasks, in particular detecting follow-up questions and finding referents for them. We characterise these tasks using the typical techniques used for performing them, and data from our corpora. We also identify a special type of follow-up question, the discourse question, which is asked when the user is trying to understand an answer, and propose some basic methods for handling it

The Intelligent Web

Many people are working on the Semantic Web with the main objective being to enhance web searches. Our proposal is a new research strategy based on the existence of a discrete set of semantic relations for the creation and exploitation of semantic networks on the web. To do so, we have defined in a previous paper (Álamo, Martínez, Jaén) the Rhetoric-Semantic Relation (RSR) based on the results of the Rhetoric Structure Theory. We formulate a general set of RSR capable of building discourse and making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. These knowledge nodes can then be elaborated in the same way. This network structure in terms of RSR makes the objective of developing automatic answering systems possible as well as any other type of utilities oriented towards the exploitation of semantic structure, such as the automatic production of web pages or automatic e-learning generation

Comprehensibility and the basic structures of dialogue

The study of what makes utterances difficult or easy to understand is one of the central topics of research in comprehension. It is both theoretically attractive and useful in practice. The more we know about difficulties in understanding the more we know about understanding. And the better we grasp typical problems of understanding in certain types of discourse and for certain recipients the better we can overcome these problems and the better we can advise people whose job it is to overcome such problems. It is therefore not surprising that comprehensibility has been the object of much reflection as far back as the days of classical rhetoric and that it is a center of lively interest in several present-day scientific disciplines, ranging from artificial intelligence and educational psychology to linguistics

Predicting Second and Third Graders' Reading Comprehension Gains: Observing Students' and Classmates Talk during Literacy Instruction using COLT.

This paper introduces a new observation system that is designed to investigate students' and teachers' talk during literacy instruction, Creating Opportunities to Learn from Text (COLT). Using video-recorded observations of 2nd-3rd grade literacy instruction (N=51 classrooms, 337 students, 151 observations), we found that nine types of student talk ranged from using non-verbal gestures to generating new ideas. The more a student talked, the greater were his/her reading comprehension (RC) gains. Classmate talk also predicted RC outcomes (total effect size=0.27). We found that 11 types of teacher talk ranged from asking simple questions to encouraging students' thinking and reasoning. Teacher talk predicted student talk but did not predict students' RC gains directly. Findings highlight the importance of each student's discourse during literacy instruction, how classmates' talk contributes to the learning environments that each student experiences, and how this affects RC gains, with implications for improving the effectiveness of literacy instruction

A process-oriented language for describing aspects of reading comprehension

Includes bibliographical references (p. 36-38)The research described herein was supported in part by the National Institute of Education under Contract No. MS-NIE-C-400-76-011

Mobile Phone Text Processing and Question-Answering

Mobile phone text messaging between mobile users and information services is a growing area of Information Systems. Users may require the service to provide an answer to queries, or may, in wikistyle, want to contribute to the service by texting in some information within the service’s domain of discourse. Given the volume of such messaging it is essential to do the processing through an automated service. Further, in the case of repeated use of the service, the quality of such a response has the potential to benefit from a dynamic user profile that the service can build up from previous texts of the same user. This project will investigate the potential for creating such intelligent mobile phone services and aims to produce a computational model to enable their efficient implementation. To make the project feasible, the scope of the automated service is considered to lie within a limited domain of, for example, information about entertainment within a specific town centre. The project will assume the existence of a model of objects within the domain of discourse, hence allowing the analysis of texts within the context of a user model and a domain model. Hence, the project will involve the subject areas of natural language processing, language engineering, machine learning, knowledge extraction, and ontological engineering

Feasibility report: Delivering case-study based learning using artificial intelligence and gaming technologies

This document describes an investigation into the technical feasibility of a game to support learning based on case studies. Information systems students using the game will conduct fact-finding interviews with virtual characters. We survey relevant technologies in computational linguistics and games. We assess the applicability of the various approaches and propose an architecture for the game based on existing techniques. We propose a phased development plan for the development of the game

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