The accuracy of an automated reading tutor in tracking the reader’s position is affected by phenomena at the frontier of the speech recognizer’s output as it evolves in real time. We define metrics of real-time tracking accuracy computed from the recognizer’s successive partial hypotheses, in contrast to previous metrics computed from the final hypothesis. We analyze the resulting considerable loss in real-time accuracy, and propose and evaluate a method to address it. Our method raises real-time accuracy from 58% to 70%, which should improve the quality of the tutor’s feedback.

In the area of student knowledge assessment, knowledge tracing is a model that has been used for over a decade to predict student knowledge and performance. Many modifications to this model have been proposed and evaluated, however, the modifications are often based on a combination of intuition and experience in the domain. This method of model improvement can be difficult for researchers without high level of domain experience and furthermore, the best improvements to the model could be unintuitive ones. Therefore, we propose a completely data driven approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvement. Our results suggest a variety of different improvements to knowledge tracing many of which have not been explored.

Different studies provide evidence that the computational psycholinguistic algorithm called Latent Semantic Analysis (LSA) allows measuring local and global coherence in texts similarly to human evaluation (Foltz, Kintsch, Landauer 1998; McNamara, Cai & Louwerse 2007; McCarthy, Briner, Rus, & McNamara, 2007; McNamara, Louwerse & Jeuniaux 2009; Louwerse, McCarthy & Graesser 2010). The texts used in all these studies are written in English and correspond to scientific and literary texts. In Spanish, there are some studies using LSA that measure the semantic similarity between texts in automatic summary assessment (Pérez, Alfonseca, Rodríguez, Gliozzo, Strapparava & Magnini 2005; León, Olmos, Escudero, Cañas & Salmerón 2006; Venegas 2007, 2009, 2011); however, automatic measurement of coherence in Spanish has not yet been sufficiently investigated. The present study aimed at identifying a global and local coherence profile in a corpus of speeches in Spanish of three Latin American Heads-of-States (Perón, Castro and Pinochet), using Latent Semantic Analysis. Local coherence is calculated through the measurement of implicit semantic similarity between adjacent sentences and global coherence through the measurement of the similarity among the semantic content of the paragraphs. The corpus under analysis corresponds to a sample of 107 speeches. The semantic space was built using a multi-register corpus and it is available through the “Interface for the measurement of lexical-semantic similarity” in the El Grial interface (www.elgrial.cl). Results showed a systematic difference between the speeches of the Heads-of-State in terms of both local and global coherence. The Bonferroni analysis established an effect that distinguishes Perón’s speeches from Pinochet’s and Castro’s speeches. This results show that Perón’s speeches are more topically related than the other leaders’, probably due to a discourse strategy to persuade voters. The identification of a profile of coherence might be relevant to predict cues of government discourse styles.

Current approaches for word-sense disambiguation (WSD) try to relate the senses of the target words by optimizing a score for each sense in the context of all other words' senses. However, by scoring each sense separately, they often fail to optimize the relations between the resulting senses. We address this problem by proposing a HITS-inspired method that attempts to optimize the score for the entire sense combination rather than one-word-at-a-time. We also exploit word-sense disambiguation via topic-models, when retrieving senses from heterogeneous sense inventories. Although this entails the relaxation of several assumptions behind current WSD algorithms, we show that our proposed method E-WSD achieves better results than current state-of-the-art approaches, without the need for additional background knowledge.

The paper investigates a phenomenon of free word order through the analysis by reduction. It exploits its formal background and data types and studies the word order freedom by means of the minimal number of word order shifts (word order changes preserving syntactic correctness, individual word forms, their morphological characteristics and/or their surface dependency relations). The investigation focuses upon an interplay of two phenomena related to word order: (non-)projectivity of a sentence and number of word order shifts within the analysis by reduction. This interplay is exemplified on a sample of Czech sentences with clitics.

Chairs and committees of the FLAIRS-25 conference.

In NL2OCL project, we aim to translate English specification of constraints to formal constraints such as OCL (Object Constraint Language). In English to OCL translation, our contribution is a semantic analyzer that uses the output of the Stanford parser for shallow and deep semantic parsing. Our analysis of the output of shallow semantic parsing showed that semantic roles were mis-identified for a few English constraints due to semantic ambiguity. Similarly, in deep semantic parsing, it is difficult to resolve scope of quantifier operators due to scope ambiguity that is another sub-type of semantic ambiguity. In this paper, we highlight the identified cases of semantic ambiguities in English constraints. We also present a novel approach to automatically resolve the identified cases of the semantic ambiguities. The presented approach is also evaluated to show that by addressing the identified cases of semantic ambiguities, we can generate more accurate and complete formal (OCL) specifications.

Applicative languages (Church’s ?-calculus and Curry’s combinatory Logic) and functional types are useful logical tools for studying and representing the meanings of verbal predicates and other linguistic operators (prepositions, preverbs …) of natural languages by means of combinations of abstract and cultural primitives. The situations are semantic expressions associated to sentences; they are written by means of applicative expressions (ae) generated from semantic abstract primitives: (i) cognitive basic types (individual, massive, distributive class, abstract places, activity, situations…); (ii) operators transforming assigned types (as topological operators : take the interior, exterior, boundary, closure of an abstract place); (iii) kinematic, dynamic, cause relators:  MOVT and CHANG expressing movement or change the state of an entity; FAIRE, CONTR (to control) and TELEO (to intend a teleonomic situation) introducing a link between a kinematic situation and an entity (agent, intermediary instrument…); CAUSE establishing a link between two different situations (a cause and an effect). These abstract primitives are interpreted inside of the cognitive fields of perception and action. They are sources of numerous grammaticalizations in languages. Verbal predicates involve an actualization over topological intervals of instants; thus, it is necessary to introduce complex operators for transforming a situation into an aspectual situation (state, event, process …). This article presents systematically these abstract primitives with some examples of meanings represented inside the applicative framework. The applicative expressions of situations (semantic schemes) defined to a semantic level can be integrated into lexical predicates of another level, by using combinators of combinatory logic; this integration process in Cognitive and Applicative Grammar (GAC) has already been presented (in precedent FLAIRS).

Using natural language as a way for students to interact with an ITS has many advantages. However, creating the intelligence with which the tutor evaluates a student’s natural language input is challenging. We describe a system, ConceptGrid, that allows non-programmers to create the instruction for checking natural language input. Three tutor authors used the system to develop answer templates for conceptual-based questions in statistics. Results indicate ConceptGrid is a viable system for non-programmers to use to allow students to use natural language to interact with a tutor.

The regularization path of the Lasso can be shown to be piecewise linear, making it possible to "follow" and explicitly compute the entire path. We analyze in this paper this popular strategy, and prove that its worst case complexity is exponential in the number of variables. We then oppose this pessimistic result to an (optimistic) approximate analysis: We show that an approximate path with at most O(1/sqrt(epsilon)) linear segments can always be obtained, where every point on the path is guaranteed to be optimal up to a relative epsilon-duality gap. We complete our theoretical analysis with a practical algorithm to compute these approximate paths.