Language Acquisition in Computers

This project explores the nature of language acquisition in computers, guided by techniques similar to those used in children. While existing natural language processing methods are limited in scope and understanding, our system aims to gain an understanding of language from first principles and hence minimal initial input. The first portion of our system was implemented in Java and is focused on understanding the morphology of language using bigrams. We use frequency distributions and differences between them to define and distinguish languages. English and French texts were analyzed to determine a difference threshold of 55 before the texts are considered to be in different languages, and this threshold was verified using Spanish texts. The second portion of our system focuses on gaining an understanding of the syntax of a language using a recursive method. The program uses one of two possible methods to analyze given sentences based on either sentence patterns or surrounding words. Both methods have been implemented in C++. The program is able to understand the structure of simple sentences and learn new words. In addition, we have provided some suggestions regarding future work and potential extensions of the existing program.Comment: 39 pages, 10 figures and 6 table

Vocabulary learning through vocabulary scrapbook

For the last 25 years, the field of English language teaching has witnessed significant responses to the incorporation of vocabulary learning in the language classroom. Vocabulary learning has been viewed as central to language learning and being of critical importance to the typical language learner. According to Coady (1997), there is a general agreement among vocabulary learning advocators that the heart of communicative competence is lexical knowledge. Such shift in emphasis in the field of ELT, followed by continuous research on vocabulary learning, have shed light on, and have provided valuable information about what to do and what to focus on. All these imply that the teachers in the language classrooms can utilise many interesting and creative techniques in vocabulary teaching and learning. A project called Vocabulary Scrapbook was introduced to the first semester students at Universiti Kuala Lumpur, Malaysia France Institute with the aim to enrich students’ vocabulary inventories via specific vocabulary learning strategies. This paper describes how the principles underlying vocabulary learning are put into practice in the project, the problems faced by the teachers and students in carrying out the project, and the effectiveness of the project in improving students’ inventories of words and phrases. A survey carried out after the project was completed revealed the students’ positive reception of the project – viewing it as a useful tool in learning and enriching their vocabulary

SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks

In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a deeply structured symbolic analysis, we use a flat connectionist analysis. This screening approach aims at supporting speech and language processing by using (1) data-driven learning and (2) robustness of connectionist networks. In order to test this approach, we have developed the SCREEN system which is based on this new robust, learned and flat analysis. In this paper, we focus on a detailed description of SCREEN's architecture, the flat syntactic and semantic analysis, the interaction with a speech recognizer, and a detailed evaluation analysis of the robustness under the influence of noisy or incomplete input. The main result of this paper is that flat representations allow more robust processing of spontaneous spoken language than deeply structured representations. In particular, we show how the fault-tolerance and learning capability of connectionist networks can support a flat analysis for providing more robust spoken-language processing within an overall hybrid symbolic/connectionist framework.Comment: 51 pages, Postscript. To be published in Journal of Artificial Intelligence Research 6(1), 199