Incremental Construction of an Associative Network from a Corpus

This paper presents a computational model of the incremental construction of an associative network from a corpus. It is aimed at modeling the development of the human semantic memory. It is not based on a vector representation, which does not well reproduce the asymmetrical property of word similarity, but rather on a network representation. Compared to Latent Semantic Analysis, it is incremental which is cognitively more plausible. It is also an attempt to take into account higher-order co-occurrences in the construction of word similarities. This model was compared to children association norms. A good correlation as well as a similar gradient of similarity were found

A Computational Model of Children's Semantic Memory

A computational model of children's semantic memory is built from the Latent Semantic Analysis (LSA) of a multisource child corpus. Three tests of the model are described, simulating a vocabulary test, an association test and a recall task. For each one, results from experiments with children are presented and compared to the model data. Adequacy is correct, which means that this simulation of children's semantic memory can be used to simulate a variety of children's cognitive processes

A semantic space for modeling children's semantic memory

The goal of this paper is to present a model of children's semantic memory, which is based on a corpus reproducing the kinds of texts children are exposed to. After presenting the literature in the development of the semantic memory, a preliminary French corpus of 3.2 million words is described. Similarities in the resulting semantic space are compared to human data on four tests: association norms, vocabulary test, semantic judgments and memory tasks. A second corpus is described, which is composed of subcorpora corresponding to various ages. This stratified corpus is intended as a basis for developmental studies. Finally, two applications of these models of semantic memory are presented: the first one aims at tracing the development of semantic similarities paragraph by paragraph; the second one describes an implementation of a model of text comprehension derived from the Construction-integration model (Kintsch, 1988, 1998) and based on such models of semantic memory

A Computational Model for Simulating Text Comprehension

International audienceIn the present article, we outline the architecture of a computer program for simulating the process by which humans comprehend texts. The program is based on psycholinguistic theories about human memory and text comprehension processes, such as the onstruction-integration model (Kintsch, 1998), the latent semantic analysis theory of knowledge representation (Landauer & Dumais, 1997), and the predication algorithms (Kintsch, 2001; Lemaire & Bianco, 2003), and it is intended to help psycholinguists investigate the way humans comprehend texts

An improved interface for tutorial dialogues: browsing a visual dialogue history

When participating in tutorial dialogues, human tutors freely refer to their own previous explanations. Explanation is an inherently incremental and interactive process. New information must be highlighted and related to what has already been presented. If user interfaces are to reap the benefits of natural language interaction, they must be endowed with the properties that make human natural language interaction so effective. This paper describes the design of a user interface that enables both the system and the user to refer to the past dialogue. The work is based on the notion that the dialogue history is a source of knowledge that can be manipulated like any other. In particular, we describe an interface that allows students to visualize the dialogue history on the screen, highlight its relevant parts and query and manipulate the dialogue history. We expect that these facilities will increase the effectiveness of the student learning of the task. KEYWORDS: Tutorial interactions, ..