Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), involve an iterative process of model optimization to identify different types of words or semantic entities. This optimization to achieve a more precise model becomes computationally difficult as the number of iterations increase. The small datasets available for training typically limit the models. Adding iterations on such sets to further optimize the model can often cause over-fitting, which generally leads to reduced performance. Therefore, the choice of convergence criteria is a critical step in robust and accurate model building. We evaluate different convergence criteria in terms of their robustness, stopping threshold selection, and independence from the training data size and entity. The underlying framework employs a limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) parameter optimization in the context of Conditional Random Fields (CRF). This paper presents a convergence criterion for robust training irrespective of semantic types and data sizes with two-orders of magnitude reduction in stopping threshold for improved model accuracy and faster convergence. Additionally, we examine convergence with active learning to further reduce the training data and training time.

Within a Question Answering (QA) framework, Question Context plays a vital role. We define Question Context to be background knowledge that can be used to represent the user’s information need more completely than the terms in the query alone. This paper proposes a novel approach that uses statistical language modeling techniques to develop a semantic Question Context which we then incorporate into the Information Retrieval (IR) stage of QA. Our approach proposes an Aspect-Based Relevance Language Model as basis of the Question Context Model. This model proposes that the sparse vocabulary of a query can be supplemented with semantic information from concepts (or aspects) related to query terms that already exist within the corpus. We incorporate the Aspect-Based Relevance Language Model into Question Context by first obtaining all of the latent concepts that exist in the corpus for a particular question topic. Then, we derive a likelihood of relevance that relates each Context Term (CT) associated with those aspects to the user’s query. Context Terms from the topics with the highest likelihood of relevance are then incorporated into the query language model based on their relevance score values. We use both query expansion and document model smoothing techniques and evaluate our approach using the traditional recall metric. Our results are promising and show significant improvements recall at low levels of precision using the query expansion method.

Vincent Aleven Intelligent tutoring systems (ITS) are highly effective in supporting student learning, but are difficult to build. The Cognitive Tutor Authoring Tools (CTAT) project started over 6 years ago with the goals of making it easier for experienced programmers, and possible for non-programmers to create an ITS. CTAT supports tutor building through programming by demonstration, an approach that has been successful in a range of application areas, but that has been applied to only a very limited degree to ITS authoring. Using CTAT, an author creates a tutor by demonstrating correct and incorrect problem solving behaviors, rather than by writing code. The resulting tutors, called exampletracing tutors, evaluate student behavior by flexibly comparing it against the demonstrated problem-solving examples.

Conference and special track committees responsible for the 2009 FLAIRS conference.

List of officers of the FLAIRS society.

This volume contains the papers presented at the 22nd International FLAIRS Conference (FLAIRS-22) held 19-21 May 2009 on Sanibel Island, Florida, USA. The call for papers attracted 158 paper submissions, 40 to the general conference and 118 to the 10 special tracks. Over 80 percent of the papers were reviewed by at least four reviewers, and all papers by at least three. Reviewing was coordinated by the program committees of the general conference and the special tracks. The program committees finally accepted the 85 papers that appear in these proceedings, all as presented papers (21 from the general conference and 64 from the special tracks) and 29 as poster papers (6 from the general conference and 23 from the special tracks).

Many reinforcement learning exploration techniques are overly optimistic and try to explore every state. Such exploration is impossible in environments with the unlimited number of states. I propose to use simulated exploration with an optimistic model to discover promising paths for real exploration. This reduces the needs for the real exploration.

The emerging Web of Data utilizes the web infrastructure to represent and interrelate data. The foundational standards of the Web of Data include the Uniform Resource Identifier (URI) and the Resource Description Framework (RDF). URIs are used to identify resources and RDF is used to relate resources. While RDF has been posited as a logic language designed specifically for knowledge representation and reasoning, it is more generally useful if it can conveniently support other models of computing. In order to realize the Web of Data as a general-purpose medium for storing and processing the world's data, it is necessary to separate RDF from its logic language legacy and frame it simply as a data model. Moreover, there is significant advantage in seeing the Semantic Web as a particular interpretation of the Web of Data that is focused specifically on knowledge representation and reasoning. By doing so, other interpretations of the Web of Data are exposed that realize RDF in different capacities and in support of different computing models.

Graded modal logic is the formal language obtained from ordinary (propositional) modal logic by endowing its modal operators with cardinality constraints. Under the familiar possible-worlds semantics, these augmented modal operators receive interpretations such as "It is true at no fewer than 15 accessible worlds that...", or "It is true at no more than 2 accessible worlds that...". We investigate the complexity of satisfiability for this language over some familiar classes of frames. This problem is more challenging than its ordinary modal logic counterpart--especially in the case of transitive frames, where graded modal logic lacks the tree-model property. We obtain tight complexity bounds for the problem of determining the satisfiability of a given graded modal logic formula over the classes of frames characterized by any combination of reflexivity, seriality, symmetry, transitivity and the Euclidean property.

Our aim in this paper is to analyse the phenotypic effects (evolvability) of diverse coding conversion operators in an instance of the states based evolutionary algorithm (SEA). Since the representation of solutions or the selection of the best encoding during the optimization process has been proved to be very important for the efficiency of evolutionary algorithms (EAs), we will discuss a strategy of coupling more than one representation and different procedures of conversion from one coding to another during the search. Elsewhere, some EAs try to use multiple representations (SM-GA, SEA, etc.) in intention to benefit from the characteristics of each of them. In spite of those results, this paper shows that the change of the representation is also a crucial approach to take into consideration while attempting to increase the performances of such EAs. As a demonstrative example, we use a two states SEA (2-SEA) which has two identical search spaces but different coding conversion operators. The results show that the way of changing from one coding to another and not only the choice of the best representation nor the representation itself is very advantageous and must be taken into account in order to well-desing and improve EAs execution.