Images are widely used in automatic text simplification systems, Picture Exchange Communication Systems (PECS) and human-produced easy-read documents, in order to make text more accessible for people with various types of disabilities, including Autism Spectrum Disorder (ASD). People with ASD are known to experience difficulties in reading comprehension, as well as to have unusual attention patterns, which makes the development of user-centred tools for this population a challenging task. This paper presents the first study to use eye-tracking technology with ASD participants in order to evaluate text documents. Its aim is two-fold. First, it evaluates the use of images in texts and provides evidence of a significant difference in the attention patterns of participants with and without autism, with the autistic participants focusing on images more than the non-autistic ones. Sets of two types of images, photographs and symbols, are compared to establish which ones are more useful to include in simple documents. Second, the study evaluates human-produced easy-read documents, as a gold standard for accessible documents, on 20 adults with autism. The results provide an understanding of the perceived level of difficulty of easy-read documents according to this population, as well as the preferences of autistic individuals in text presentation. The results are synthesized as set of guidelines for creating accessible text for autism.

This paper introduces VIP, an R&D project that explores the impact and feasibility of using Human Language Technology (HLT) and Natural Language Processing (NLP) for interpreting training, practice and research. This project aims at filling the gap in and addressing the pressing need for technology in general for interpreters, which is reported to be scarce. Although most interpreters are unaware of interpreting technologies or are reluctant to use them, there are some tools and resources already available, mainly computer-assisted interpreting (CAI) tools. VIP is working on the development of technology and cutting-edge research with the potential to revolutionise the interpreting industry by lowering costs for interpreter training, fostering an online community which shares, generates and cultivates interpreting resources; and providing an efficient interpreter workbench tool (computerassisted interpreting software).

In recent years, Construction Grammar has emerged as an enhanced theoretical framework for studies on phraseology in general, and particularly for collocational analysis. This paper aims at contributing to the study of collocational constructions in translated Spanish. To this end, the construction [V PP_ de miedo ] is analysed in detail. Our methodology is corpus-based and compares subtitled translations with general Spanish, American Spanish and Peninsular Spanish. The findings suggest that collocational constructions in translated Spanish have a clear preference for the Peninsular standard. They reflect features of translationese, as well as universal traits such as simplification, normalisation, and convergence. Another interesting finding refers to corpus selection, as giga-token corpora appear to provide more fine-grained analysis that conventional, balanced corpora.

We describe a new technique for building a relevance judgment list (qrels) for TREC test collections with no human intervention. For each TREC topic, a set of new queries is automatically generated from key phrases extract-ed from the top k documents retrieved from 12 different Terrier weighting models when the initial TREC topic is submitted. We assign a score to each key phrase based on its similarity to the original TREC topic. The key phrases with the highest scores become the new queries for a second search, this time using the Terrier BM25 weighting model. The union of the documents retrieved forms the automatically-build set of qrels.

This paper represents a new technique for building a relevance judgment list for information retrieval test collections without any human intervention. It is based on the number of occurrences of the documents in runs retrieved from several information retrieval systems and a distance based measure between the documents. The effectiveness of the technique is evaluated by computing the correlation between the ranking of the TREC systems using the original relevance judgment list (qrels) built by human assessors and the ranking obtained by using the newly generated qrels.