Strategies for removal and introduction of combinators are very important to assure an accurate use of combinatory logic and combinators in natural language processing, especially in structural reorganization of expressions that express semantic interpretation. Such a strategy already exists for the elimination of combinators in a combinatory expression to obtain a normal form without combinators, but none existed to automate the inverse process. In our previous work, we addressed this problem by proposing an algorithm for the automation of combinators’ introduction, which finds the introduction level and introduces it at the first available spot.  However, this algorithm shows its limits.  There are some specific cases where a combinator can be introduced at more than one place.  We needed to improve our algorithm so that it can automatically find the exact path to take in order to reach the correct place where we have to introduce the combinator, and then the algorithm would work for any combinatory expression.  This paper presents the enhanced algorithm with an example of its execution.

Arabic Subject Markers are interface phenomena (specifically between morphology and syntax). In this paper, I describe them briefly, I give my linguistic analysis within the framework of the Head-Driven Phrase Structure Grammar and I show how I implement them in the LKB system. I show that this system, despite its strength, does not allow for a proper implementation of these units.

We present a work in the field of formal ontologies, notion taken from the knowledge representation community. What we study is the concept of time and aspect described and conceptualized from linguistics. Our aim is thus to propose a formal ontology of time and aspect considering temporal concepts introduced in a formal way.

In this paper we describe how the annotation methodology adopted in our approach allows us to explain the organization of indexed references in scientific research articles. We identify the semantic values of author judgments in the text segments containing indexed references.  We use an automated semantic annotation platform to annotate our corpora. Exploiting this result, we obtain a representation of the annotation distribution on different scales. Finally, we present two evaluations of the annotation.

Scientists typically need to take a large volume of information into account in order to deal with re-occurring tasks such as inspecting proceedings, finding related work, or reviewing papers. Our work aims at filling the gap between text documents and a structured representations of their content in the domain of resilience computing by combining computer linguistics and ontological methods. The results of our research include: a thesaurus of the domain, automatic clustering of the domain documents, a domain ontology, and a tool for constructing ontologies with the aid of domain thesauri.

For certain disable persons unable to communicate, we present a palliative aid which consists of a virtual pictographic keyboard associated to a text processing from a pictographic scripture. Words and the grammar are given as pictograms. The pictographic lexicon must be organized following the mental lexicon of the user to propose the pictograms of grammar in order to facilitate his (her) task of writing. We discuss the utility of ontologies in the organization of lexicons and in the building of texts.

Providing the first few letters of a missing word in a sentence gives information about this word. This paper attempts to measure the information transmitted in that case. In order to do so, we analyzed response accuracy for open cloze questions, that is fill-in-the-blank questions without multiple choice answers. In this study, native and non-native speakers of English answered a series of open cloze questions that were semi-automatically generated. Hints were provided that consisted of the first few letters of the missing word. Results showed that question difficulty, hence the quantity of information transmitted, is related to the number of letters that are provided, to physical properties of these letters and to syllables formed by these letters. Performances did not appear to depend on letter or syllable frequency. Controlling hint level in a word completion task is critical in order to provide practice exercises adapted to student levels.

We studied the effect of post-practice reflection on learning, using programming tutors, and multiple-choice format for reflection. We conducted in-vivo controlled studies with introductory programming students from multiple schools over 3 semesters, and used mixed-factor ANOVA to analyze the collected data. We found that reflecting on the concept underlying each problem neither promotes greater learning, measured as pre-post increase in the average score per problem, nor promotes faster learning, measured as the problems solved per concept learned. We conjecture that the benefits of reflecting on the concept underlying each problem may be limited if a tutor already promotes deep understanding of the domain.

Emotions are a ubiquitous component of motivation and learning. We have developed an affective behavior model for intelligent tutoring systems that considers both the affective and knowledge state of the student to generate tutorial actions. The affective behavior model (ABM) was designed based on teachers' expertise obtained through interviews. It relies on a dynamic decision network with a utility measure on both student learning and affect to generate tutorial actions aimed at balancing the two. We have integrated and evaluated the ABM in an educational game to learn number factorization. We carried out a controlled user study to evaluate the impact of the affective model on learning. The results show that for the younger students there is a significant improvement on learning when the affective behavior model is incorporated.

This paper focuses on the identification of human activity patterns in SecondLife (SL), a user-constructed virtual environment.SecondLife allows the users to create a virtual avatar,explore areas constructed by other users, socialize, and conduct financial transactions just as one would in the real world.However unlike the real world, new attractions can be constructed within hours and previous ones often fall into disuse rapidly. Without current information about the state of regions in the virtual world, it is difficult to infer the purpose of the user’s actions from location information. In this paper,we present an approach for gathering data on users’ activities and building a map of SecondLife annotated with information about activities that the users were able to perform in each region. Using this map, a recommender agent built into the user’s heads-up display can present suggestions of other areas to visit based on data collected from previous users. We discuss the the use of five supervised classifiers and report classification results for the map construction portion of the agent.