Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

Using Spaced Learning Principles to Translate Knowledge into Behavior: Evidence from Investigative Interviews of Alleged Child Abuse Victims

The present study assessed the progress of 13 investigative interviewers (child protection workers and police officers) before, during, and after an intensive training program (n = 132 interviews). Training began with a 2-day workshop covering the principles of child development and child-friendly interviewing. Interviewers then submitted interviews on a bi-weekly basis to which they received written and verbal feedback over an 8-month period. A refresher session took place two months into training. Interestingly, improvements were observed only after the refresher session. Interviews conducted post-refresher training contained proportionally more open-ended questions, more child details in response to open-ended questions, and proportionally fewer closed questions than interviews conducted prior to training and in the first half of the training program. The need for ‘spaced learning’ may underlie why so many training programs have had little effect on practice

Paraphrastic Reformulations in Spoken Corpora

International audienceOur work addresses the automatic detection of paraphrastic reformulation in French spoken corpora. The proposed approach is syn-tagmatic. It is based on specific markers and the specificities of the spoken language. Manual multi-dimensional annotation performed by two annotators provides fine-grained reference data. An automatic method is proposed in order to decide whether sentences contain or not paraphras-tic relations. The obtained results show up to 66.4% precision. Analysis of the manual annotations indicates that few paraphrastic segments show morphological modifications (inflection, derivation or compounding) and that the syntactic equivalence between the segments is seldom respected, as these usually belong to different syntactic categories

Anyone for non-scalarity?

This paper examines the status of scalarity in the analysis of the meaning of the English determiner any. The latter’s position as a prime exemplar of the category of polarity-sensitive items has led it to be generally assumed to have scalar meaning. Scalar effects are absent however from a number of common uses of this word. This suggests that any does not involve scales as part of its core meaning, but produces them as a derived interpretative property. The role of three factors in the derivation of the expressive effect of scalarity is explored: grammatical number, stress and the presence of gradable concepts in the NP. The general conclusions point to the importance of developing a causal semantic analysis in which the contributions of each of the various meaningful components of an utterance to the overall message expressed are carefully distinguished