Various strategies and techniques have been proposed for the generation of questions/answers tests in Intelligent Tutoring Systems by using OWL (Web Ontology Language) ontolo- gies. Currently there have been no known methods to utilize SWRL rules for this task. This paper presents a system and a set of strategies that can be used in order to automatically generate multiple choice questions from SWRL rules. The aim of the proposed framework is to support further research in the area and to be a testbed for the development of more advanced assessment techniques.

Agreement asymmetries are the most debated issue in Arabic linguistics. Even though the facts suggest a unified treatment based on the properties of agreement, most of the researchers in this field don’t take into account the essential difference between grammatical agreement and anaphoric agreement. We do propose such a distinction to explain these asymmetries and we propose an analysis that we implement in the ACCG framework.

Thomas K. Landauer (Pearson Knowledge Technologies) The recently created word maturity (WM) metric uses the computational language model LSA to mimic the average evolutionary growth of individual word and paragraph knowledge as a function of the total amount and order of simulated reading. The simulator traces the separate growth trajectories of an unlimited number of different words from the beginning of reading to adult level.

To overcome the problems in Thai word segmentation, a number of word segmentation has been proposed during the long period of time until today. We propose a novel Thai word segmentation approach so called Co-occurrence-Based Error Correction (CBEC). CBEC generates all possible segmentation candidates using the classical maximal matching algorithm and then selects the most accurate segmentation based on co-occurrence and an error correction algorithm. CBEC was trained and evaluated on BEST 2009 corpus.

Expressions (MWEs) (Okita et al. 2010), this may improve the overall translation. For example in EN-JP, the empirical evidences 2007; Koehn 2010) intends to handle morphologically rich suggest that we separate affix(es) and word stem(s) since it languages in the target side by integrating additional linguistic obtains better BLEU score than the case when we do not separate markup at the word level, where each type of additional them although the adequacy decreases.

Linguistic and semantic annotations are important features for text-based applications. However, achieving and maintaining a good quality of a set of annotations is known to be a complex task. Many ad hoc approaches have been developed to produce various types of annotations, while comparing those annotations to improve their quality is still rare. In this paper, we propose a framework in which both linguistic and domain information can cooperate to reason with annotations. The underlying knowledge representation issues are carefully analyzed and solved by studying a higher order logic, which accounts for the cooperation of different sorts of knowledge. Our prototype implements this logic based on a reduction to classical description logics by preserving the semantics, allowing us to benefit from cutting-edge Semantic Web reasoners. An application scenario shows interesting merits of this framework on reasoning with annotations of texts.

Negation is present in all human languages and it is used to reverse the polarity of parts of a statement. It is a complex phenomenon that interacts with many other aspects of language. Besides the direct meaning, negated statements often carry a latent positive meaning. Negation can be interpreted in terms of its scope and focus. This paper explores the importance of both scope and focus to capture the meaning of negated statements. Some issues on detecting negation from text are outlined, the forms in which negation occurs are depicted and heuristics to detect its scope and focus are proposed.

Closely related genres have complex interrelations. An antecedent genre can constrain a subsequent genre, but changing rhetorical situations can lead to distinctions between an antecedent and its descendent. In this study, we assess two genres of detective fiction to determine their hierarchical relation to one another. We use the Gramulator, a computational tool that identifies indicative lexical features, to explain the relationship between whodunit fiction and hardboiled fiction . We conclude, based on the indicative lexical features of the expositions in texts, that the two are sibling genres.

The paper argues, briefly, that much work in formal Computational Semantics (alias CompSem ) is not computational at all, and does not attempt to be; there is some mis-description going on here on a large and long-term scale. Moreover, the examples used to support its value for the representation of the meaning of language strings have no place in normal English usage, or their corpora, and this should be better understood. The recent large-scale developments in Natural Language Processing (NLP), such as machine translation or question answering, which are quite successful and undeniably semantic and computational, have made no use of such techniques. Most importantly, the Semantic Web (and Information Extraction techniques generally) now offer the possibility of large scale use of language data so as to achieve concrete results achieved by methods deemed impossible in formal semantics, namely annotation methods that are fundamentally forms of Lewis’ (1970) “markerese.”

Learners may develop expertise by experiencing numerous different but relevant situations. Computer games and virtual simulations can facilitate these training opportunities, however, because of the relative difficulty in authoring new scenarios, the increasing need for new and different scenarios becomes a bottleneck in the learning process. Furthermore, a one-size-fits-all scenario may not address all of the abilities, needs, or goals of a particular learner. To address these issues we present a novel technique, Automated Scenario Adaptation, to automatically “rewrite” narrative scenario content to suit individual learners’ needs and abilities and to incorporate recent changes from real world learning needs. Scenario adaptation acts as problem generation for intelligent tutoring systems, producing greater learning opportunities that facilitate engagement and continued learner involvement.