We present a new summarisation task, taking scientific articles and producing journal tableof-contents entries in the chemistry domain.These are one-or two-sentence author-written summaries that present the key findings of a paper.This is a first look at this summarisation task with an open access publication corpus consisting of titles and abstracts, as input texts, and short author-written advertising blurbs, as the ground truth.We introduce the dataset and evaluate it with state-of-the-art summarisation methods.

This article introduces RELPRON, a large data set of subject and object relative clauses, for the evaluation of methods in compositional distributional semantics. RELPRON targets an intermediate level of grammatical complexity between content-word pairs and full sentences. The task involves matching terms, such as “wisdom,” with representative properties, such as “quality that experience teaches.” A unique feature of RELPRON is that it is built from attested properties, but without the need for them to appear in relative clause format in the source corpus. The article also presents some initial experiments on RELPRON, using a variety of composition methods including simple baselines, arithmetic operators on vectors, and finally, more complex methods in which argument-taking words are represented as tensors. The latter methods are based on the Categorial framework, which is described in detail. The results show that vector addition is difficult to beat—in line with the existing literature—but that an implementation of the Categorial framework based on the Practical Lexical Function model is able to match the performance of vector addition. The article finishes with an in-depth analysis of RELPRON, showing how results vary across subject and object relative clauses, across different head nouns, and how the methods perform on the subtasks necessary for capturing relative clause semantics, as well as providing a qualitative analysis highlighting some of the more common errors. Our hope is that the competitive results presented here, in which the best systems are on average ranking one out of every two properties correctly for a given term, will inspire new approaches to the RELPRON ranking task and other tasks based on linguistically interesting constructions.

Distributional semantic models (DSMs) have been effective at representing semantics at the word level, and research has recently moved on to building distributional representations for larger segments of text.In this paper, we introduce novel ways of applying context selection and normalisation to vary model sparsity and the range of values of the DSM vectors.We show how these methods enhance the quality of the vectors and thus result in improved low dimensional and composed representations.We demonstrate these effects on standard word and phrase datasets, and on a new definition retrieval task and dataset.

Compositional distributional semantics is a subfield of Computational Linguistics which investigates methods for representing the meanings of phrases and sentences.In this paper, we explore implementations of a framework based on Combinatory Categorial Grammar (CCG), in which words with certain grammatical types have meanings represented by multilinear maps (i.e.multi-dimensional arrays, or tensors).An obstacle to full implementation of the framework is the size of these tensors.We examine the performance of lower dimensional approximations of transitive verb tensors on a sentence plausibility/selectional preference task.We find that the matrices perform as well as, and sometimes even better than, full tensors, allowing a reduction in the number of parameters needed to model the framework.

As adults we take for granted our capacity to express our information needs verbally and textually. However, young children also have preferences and information needs, but are just learning to be able to express themselves effectively. Consequently they encounter many barriers when trying to spell, type, and communicate their needs to a 'faceless' search engine text box.

Children face several challenges when using information access systems. These include formulating queries, judging the relevance of documents, and focusing attention on interface cues, such as query suggestions, while typing queries. It has also been shown that children want a personalised Web experience and prefer content presented to them that matches their long-term entertainment and education needs. To this end, we have developed an interaction-based information filtering system to address these challenges.