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

Computational Cognitive Models of Summarization Assessment Skills

This paper presents a general computational cognitive model of the way a summary is assessed by teachers. It is based on models of two subprocesses: determining the importance of sentences and guessing the cognitive rules that the student may have used. All models are based on Latent Semantic Analysis, a computational model of the representation of the meaning of words and sentences. Models' performances are compared with data from an experiment conducted with 278 middle school students. The general model was implemented in a learning environment designed for helping students to write summaries

From production to selection of interpretations for novel conceptual combinations: A developmental approach

This study looks at how combinations of two French nouns are interpreted. The order of occurrence of the constituents of two types of conceptual combinations, relation and property, was manipulated in view of determining how property-based and relation-based interpretations evolve with age. Three groups of French-speaking children (ages 6, 8, and 10) and a group of adults performed an interpretation-selection task. The results for the children indicated that while property-based interpretations increased with age, relation-based interpretations were in the majority for both combination types, whereas for the adults, relation-based interpretations were in the minority for property combinations. For the children and adults alike, the most frequent interpretations were ones in which the head noun came first and was followed by the modifier (the opposite of the order observed for English)

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

Effect of Tuned Parameters on a LSA MCQ Answering Model

This paper presents the current state of a work in progress, whose objective is to better understand the effects of factors that significantly influence the performance of Latent Semantic Analysis (LSA). A difficult task, which consists in answering (French) biology Multiple Choice Questions, is used to test the semantic properties of the truncated singular space and to study the relative influence of main parameters. A dedicated software has been designed to fine tune the LSA semantic space for the Multiple Choice Questions task. With optimal parameters, the performances of our simple model are quite surprisingly equal or superior to those of 7th and 8th grades students. This indicates that semantic spaces were quite good despite their low dimensions and the small sizes of training data sets. Besides, we present an original entropy global weighting of answers' terms of each question of the Multiple Choice Questions which was necessary to achieve the model's success.Comment: 9 page