Answering Complex Questions by Joining Multi-Document Evidence with Quasi Knowledge Graphs

Direct answering of questions that involve multiple entities and relations is a challenge for text-based QA. This problem is most pronounced when answers can be found only by joining evidence from multiple documents. Curated knowledge graphs (KGs) may yield good answers, but are limited by their inherent incompleteness and potential staleness. This paper presents QUEST, a method that can answer complex questions directly from textual sources on-the-fly, by computing similarity joins over partial results from different documents. Our method is completely unsupervised, avoiding training-data bottlenecks and being able to cope with rapidly evolving ad hoc topics and formulation style in user questions. QUEST builds a noisy quasi KG with node and edge weights, consisting of dynamically retrieved entity names and relational phrases. It augments this graph with types and semantic alignments, and computes the best answers by an algorithm for Group Steiner Trees. We evaluate QUEST on benchmarks of complex questions, and show that it substantially outperforms state-of-the-art baselines

The Learning Grid and E-Assessment using Latent Semantic Analysis

E-assessment is an important component of e-learning and e-qualification. Formative and summative assessment serve different purposes and both types of evaluation are critical to the pedagogicalprocess. While students are studying, practicing, working, or revising, formative assessment provides direction, focus, and guidance. Summative assessment provides the means to evaluate a learner's achievement and communicate that achievement to interested parties. Latent Semantic Analysis (LSA) is a statistical method for inferring meaning from a text. Applications based on LSA exist that provide both summative and formative assessment of a learner's work. However, the huge computational needs are a major problem with this promising technique. This paper explains how LSA works, describes the breadth of existing applications using LSA, explains how LSA is particularly suited to e-assessment, and proposes research to exploit the potential computational power of the Grid to overcome one of LSA's drawbacks

From corpus-based collocation frequencies to readability measure

This paper provides a broad overview of three separate but related areas of research. Firstly, corpus linguistics is a growing discipline that applies analytical results from large language corpora to a wide variety of problems in linguistics and related disciplines. Secondly, readability research, as the name suggests, seeks to understand what makes texts more or less comprehensible to readers, and aims to apply this understanding to issues such as text rating and matching of texts to readers. Thirdly, collocation is a language feature that occurs when particular words are used frequently together for other than purely grammatical reasons. The intersection of these three aspects provides the basis for on-going research within the Department of Computer and Information Sciences at the University of Strathclyde and is the motivation for this overview. Specifically, we aim through analysis of collocation frequencies in major corpora, to afford valuable insight on the content of texts, which we believe will, in turn, provide a novel basis for estimating text readability

Learning Language from a Large (Unannotated) Corpus

A novel approach to the fully automated, unsupervised extraction of dependency grammars and associated syntax-to-semantic-relationship mappings from large text corpora is described. The suggested approach builds on the authors' prior work with the Link Grammar, RelEx and OpenCog systems, as well as on a number of prior papers and approaches from the statistical language learning literature. If successful, this approach would enable the mining of all the information needed to power a natural language comprehension and generation system, directly from a large, unannotated corpus.Comment: 29 pages, 5 figures, research proposa

THE EFFECT OF READING TEACHING MATERIAL FOR DIFFERENT LEARNING STYLES IN IMPROVING STUDENTS’ READING COMPREHENSION

This study aims to explain the relationship between student learning styles and student achievement in English Reading Comprehension and to determine how extents the teaching material meets the needs of students with different learning styles. Researchers conducted the One-Group Pretest-Posttest design where only one experimental group was given a pre-test and post-test. Data collection is done in several ways, namely by giving a questionnaire from Barsch Learning style inventory (1980), interviewing and conducting pre and post tests to determine the development of students' abilities before and after the presentation of the material. In addition, classroom observation was conducted to find out the classroom activities and students’ participation toward teaching material given. The data  revealed that the average of students’ result in pre-test was 38.92 % while the result of the average of students’ result in post-test was 68.58 %.  It may say that the students’ improvement in reading comprehension using the teaching material has a significant improvement. 100% of visual learners have improved the ability in English reading comprehension after being given the material. 100% of auditory learners also have an increase in the ability in English reading comprehension after the provision of teaching materials. Likewise with visual-auditory learners in which 100 percent of respondents experienced an increase. However, only 80 percent of 100 percent kinesthetic learners experience an increase after the provision of teaching materials. There are many factors that influence the students. It can be in the form of internal factors that trigger students' motivation in learning English