Introduction to the special issue on cross-language algorithms and applications

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version

Methods for Amharic part-of-speech tagging

The paper describes a set of experiments involving the application of three state-of- the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for Eng- lish, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy ap- proach, while HMM-based and SVM- based taggers got comparable results

What forms the chunks in a subject's performance? Lessons from the CHREST computational model of learning

Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk

Optimal line length for reading schoolbook on screen

Although experimental studies have shown a strong impact of text layout on the legibility of e- text, many digital texts appearing in eBook or the internet use different designs, so that there is no straightforward answer in the literature over which one to follow when designing e- material. Therefore, in this paper we shall focus on the text layout, particularly the influence of line lengthen reading performance of e-school book.48 native Arabic students (24 male and 24 female) volunteered for this experiment. The participants’ age ranged from 9 to 13. Performance of students was assessed through two dependent variables: (1) time to complete each tasks; and (2) accuracy of the answers. Accuracy data was based on the number of correct answers the students provided and the total score was 12 points. Several findings were reported by this experiment such as; the time needed to complete all the question models becomes significantly low when students are older, errors for all the question models are expected to be significantly lower for older students. Reading text on a single column with double columns shows that the reading process is affected by the students’ age, as older students were faster when reading through double columns, while students aged 9 prefer the single column in both reading processes. The study has recommended double line for fast reading for students their reading performance is satisfactory. While, long line has suggested for students with difficulty in reading

Arabic Query Expansion Using WordNet and Association Rules

Query expansion is the process of adding additional relevant terms to the original queries to improve the performance of information retrieval systems. However, previous studies showed that automatic query expansion using WordNet do not lead to an improvement in the performance. One of the main challenges of query expansion is the selection of appropriate terms. In this paper, we review this problem using Arabic WordNet and Association Rules within the context of Arabic Language. The results obtained confirmed that with an appropriate selection method, we are able to exploit Arabic WordNet to improve the retrieval performance. Our empirical results on a sub-corpus from the Xinhua collection showed that our automatic selection method has achieved a significant performance improvement in terms of MAP and recall and a better precision with the first top retrieved documents