A Machine Learning Approach For Opinion Holder Extraction In Arabic Language

Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research

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

TRANSDUCER FOR AUTO-CONVERT OF ARCHAIC TO PRESENT DAY ENGLISH FOR MACHINE READABLE TEXT: A SUPPORT FOR COMPUTER ASSISTED LANGUAGE LEARNING

There exist some English literary works where some archaic words are still used; they are relatively distinct from Present Day English (PDE). We might observe some archaic words that have undergone regular changing patterns: for instances, archaic modal verbs like mightst, darest, wouldst. The –st ending historically disappears, resulting on might, dare and would. (wouldst > would). However, some archaic words undergo distinct processes, resulting on unpredictable pattern; The occurrence frequency for archaic english pronouns like thee ‘you’, thy ‘your’, thyself ‘yourself’ are quite high. Students that are Non-Native speakers of English might come across many difficulties when they encounter English texts which include these kinds of archaic words. How might computer be a help for the student? This paper aims on providing some supports from the perspective of Computer Assisted Language Learning (CALL). It proposes some designs of lexicon transducers by using Local Grammar Graphs (LGG) for auto-convert of the archaic words to PDE in a literature machine readable text. The transducer is applied to a machine readable text that is taken from Sir Walter Scott’s Ivanhoe. The archaic words in the corpus can be converted automatically to PDE. The transducer also allows the presentation of the two forms (Arhaic and PDE), the PDE lexicons-only, or the original (Archaic Lexicons) form-only. This will help students in understanding English literature works better. All the linguistic resources here are machine readable, ready to use, maintainable and open for further development. The method might be adopted for lexicon tranducer for another language too