Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

Queensland University of Technology at TREC 2005

The Information Retrieval and Web Intelligence (IR-WI) research group is a research team at the Faculty of Information Technology, QUT, Brisbane, Australia. The IR-WI group participated in the Terabyte and Robust track at TREC 2005, both for the first time. For the Robust track we applied our existing information retrieval system that was originally designed for use with structured (XML) retrieval to the domain of document retrieval. For the Terabyte track we experimented with an open source IR system, Zettair and performed two types of experiments. First, we compared Zettair’s performance on both a high-powered supercomputer and a distributed system across seven midrange personal computers. Second, we compared Zettair’s performance when a standard TREC title is used, compared with a natural language query, and a query expanded with synonyms. We compare the systems both in terms of efficiency and retrieval performance. Our results indicate that the distributed system is faster than the supercomputer, while slightly decreasing retrieval performance, and that natural language queries also slightly decrease retrieval performance, while our query expansion technique significantly decreased performance

Hierarchical Character-Word Models for Language Identification

Social media messages' brevity and unconventional spelling pose a challenge to language identification. We introduce a hierarchical model that learns character and contextualized word-level representations for language identification. Our method performs well against strong base- lines, and can also reveal code-switching

Applied Software Tools for Supporting Children with Intellectual Disabilities

We explored the level of technology utilization in supporting children with cognitive disabilities at schools, speech clinics, and with assistive communication at home. Anecdotal evidence, literature research, and our own survey of special needs educators in Central Florida reveal that use of technology is minimal in classrooms for students with special needs even when scientific research has shown the effectiveness of video modeling in teaching children with special needs new skills and behaviors. Research also shows that speech and language therapists utilize a manual approach to elicit and analyze language samples from children with special needs. While technology is utilized in augmentative and alternative communication, many caregivers utilize paper-based picture exchange systems, storyboards, and daily schedules when assisting their children with their communication needs. We developed and validated three software frameworks to aid language therapists, teachers, and caregivers in supporting children with cognitive disabilities and related special needs. The Analysis of Social Discourse Framework proposes that language therapists use social media discourse instead of direct elicitation of language samples. The framework presents an easy-to-use approach to analyzing language samples based on natural language processing. We validated the framework by analyzing public social discourse from three unrelated sources. The Applied Interventions for eXceptional-needs (AIX) framework allows classroom teachers to implement and track interventions using easy-to-use smartphone applications. We validated the framework by conducting a sixteen-week pilot case study in a school for students with special needs in Central Florida. The Language Enhancements for eXceptioanl Youth (LEXY) framework allows for the development of a new class of augmentative and alternative communication tools that are based on conversational chatbots that assist children with special needs while utilizing a model of the world curated by their caregivers. We validated the framework by simulating an interaction between a prototype chatbot that we developed, a child with special needs, and the child\u27s caregiver