Burning questions, but no burning answers: collocation and idiomaticity

The attention of linguists has increasingly shifted from grammar to lexis. Collocation has emerged as a key feature of lexis. Research using large language corpora has not only helped to identify the significant collocates of individual words but also to confirm the importance of collocation in the language system. John Sinclair has suggested that language operates on two principles: open choice and idiom. If so, then collocation would appear to be the minimal level of idiomaticity. One problem with collocation is that words that habitually co-occur form less distinct, often discontinuous, idiomatic units, whereas grammar generally works with more precisely delineated and contiguous structural units. This paper uses examples from corpus evidence to look at various aspects of collocation.

Questions and answers:Landlord and tenant Law

The law of landlord and tenant has become an increasingly complex area for both professionals and students. Apart from the double hurdle of mastering both common law principles and statutory codes, various aspects of the subject have become increasingly specialised and challenging. This new edition of Question and Answer Landlord and Tenant demonstrates that even complex problems can be explained in straightforward and inspiring terms. The authors, both experienced academics and barristers, provide detailed answers to typical questions in this difficult field. The third edition of this book has been updated in the new Question and Answer style of questions followed by commentary, bullet points and diagrams and flowcharts. It offers new questions based on the latest recommendations of the Law Commission on renting homes and the abolition of the law of forfeiture. There are new questions on the human rights dimension, the recent changes to Part II of the Landlord and Tenant Act 1954 and the substantial amendments made to leasehold enfranchisement under the Commonhold and Leasehold Reform Act 2002.

Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment

Background - The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. Objectives - The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. Methods - We conducted a randomized experiment, assigning students randomly to receive PR or non–PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re–use statistical results from peers, Collaborative PR, and an AI–enhanced Stock Market Engine. Results - The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non–Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.

Automatic identification of best answers in online enquiry communities

Online communities are prime sources of information. The Web is rich with forums and Question Answering (Q&A) communities where people go to seek answers to all kinds of questions. Most systems employ manual answer-rating procedures to encourage people to provide quality answers and to help users locate the best answers in a given thread. However, in the datasets we collected from three online communities, we found that half their threads lacked best answer markings. This stresses the need for methods to assess the quality of available answers to: 1) provide automated ratings to fill in for, or support, manually assigned ones, and; 2) to assist users when browsing such answers by filtering in potential best answers. In this paper, we collected data from three online communities and converted it to RDF based on the SIOC ontology. We then explored an approach for predicting best answers using a combination of content, user, and thread features. We show how the influence of such features on predicting best answers differs across communities. Further we demonstrate how certain features unique to some of our community systems can boost predictability of best answers.

Merging and ranking answers in the semantic web:the wisdom of crowds

In this paper we propose algorithms for combining and ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. Our proposal includes a merging algorithm that aggregates, combines and filters ontology-based search results and three different ranking algorithms that sort the final answers according to different criteria such as popularity, confidence and semantic interpretation of results. An experimental evaluation on a large scale corpus indicates improvements in the quality of the search results with respect to a scenario where the merging and ranking algorithms were not applied. These collective methods for merging and ranking allow to answer questions that are distributed across ontologies, while at the same time, they can filter irrelevant answers, fuse similar answers together, and elicit the most accurate answer(s) to a question.