Word order variation and string similarity algorithm to reduce pattern scripting in pattern matching conversational agents

This paper presents a novel sentence similarity algorithm designed to tackle the issue of free word order in the Urdu language. Free word order in a language poses many challenges when implemented in a conversational agent, primarily due to the fact that it increases the amount of scripting time needed to script the domain knowledge. A language with free word order like Urdu means a single phrase/utterance can be expressed in many different ways using the same words and still be grammatically correct. This led to the research of a novel string similarity algorithm which was utilized in the development of an Urdu conversational agent. The algorithm was tested through a black box testing methodology which involved processing different variations of scripted patterns through the system to gauge the performance and accuracy of the algorithm with regards to recognizing word order variations of the related scripted patterns. Initial testing has highlighted that the algorithm is able to recognize legal word order variations and reduce the knowledge base scripting of conversational agents significantly. Thus saving great time and effort when scripting the knowledge base of a conversational agent

Semantic similarity framework for Thai conversational agents

Conversational Agents integrate computational linguistics techniques and natural language to support human-like communication with complex computer systems. There are a number of applications in business, education and entertainment, including unmanned call centres, or as personal shopping or navigation assistants. Initial research has been performed on Conversational Agents in languages other than English. There has been no significant publication on Thai Conversational Agents. Moreover, no research has been conducted on supporting algorithms for Thai word similarity measures and Thai sentence similarity measures. Consequently, this thesis details the development of a novel Thai sentence semantic similarity measure that can be used to create a Thai Conversational Agent. This measure, Thai Sentence Semantic Similarity measure (TSTS) is inspired by the seminal English measure, Sentence Similarity based on Semantic Nets and Corpus Statistics (STASIS). A Thai sentence benchmark dataset, called 65 Thai Sentence pairs benchmark dataset (TSS-65), is also presented in this thesis for the evaluation of TSTS. The research starts with the development a simple Thai word similarity measure called TWSS. Additionally, a novel word measure called a Semantic Similarity Measure, based on a Lexical Chain Created from a Search Engine (LCSS), is also proposed using a search engine to create the knowledge base instead of WordNet. LCSS overcomes the problem that a prototype version of Thai Word semantic similarity measure (TWSS) has with the word pairs that are related to Thai culture. Thai word benchmark datasets are also presented for the evaluation of TWSS and LCSS called the 30 Thai Word Pair benchmark dataset (TWS-30) and 65 Thai Word Pair benchmark dataset (TWS-65), respectively. The result of TSTS is considered a starting point for a Thai sentence measure which can be illustrated to create semantic-based Conversational Agents in future. This is illustrated using a small sample of real English Conversational Agent human dialogue utterances translated into Thai

The development of a fuzzy semantic sentence similarity measure

A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent the effect perception based (fuzzy) words, which are commonly used in natural language, have on sentence similarity. This research project developed a new sentence similarity measure to solve this problem. The new measure, Fuzzy Algorithm for Similarity Testing (FAST) is a novel ontology-based similarity measure that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. Through human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the fuzzy words contained within them and from that a new fuzzy ontology was created. Using these relationships allows for the creation of a new ontology-based fuzzy semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. In order to evaluate FAST, two new test datasets were created through the use of questionnaire based human experimentation. This involved the generation of a robust methodology for creating usable fuzzy datasets (including an automated method that was used to create one of the two fuzzy datasets). FAST was evaluated through experiments conducted using the new fuzzy datasets. The results of the evaluation showed that there was an improved level of correlation between FAST and human test results over two existing sentence similarity measures demonstrating its success in representing the similarity between pairs of sentences containing fuzzy words

Methodology and algorithms for Urdu language processing in a conversational agent

This thesis presents the research and development of a novel text based goal-orientated conversational agent (CA) for the Urdu language called UMAIR (Urdu Machine for Artificially Intelligent Recourse). A CA is a computer program that emulates a human in order to facilitate a conversation with the user. The aim is investigate the Urdu language and its lexical and grammatical features in order to, design a novel engine to handle the language unique features of Urdu. The weakness in current Conversational Agent (CA) engines is that they are not suited to be implemented in other languages which have grammar rules and structure totally different to English. From a historical perspective CA’s including the design of scripting engines, scripting methodologies, resources and implementation procedures have been implemented for the most part in English and other Western languages (i.e. German and Spanish). The development of an Urdu conversational agent has required the research and development of new CA framework which incorporates methodologies and components in order overcome the language unique features of Urdu such as free word order, inconsistent use of space, diacritical marks and spelling. The new CA framework was utilised to implement UMAIR. UMAIR is a customer service agent for National Database and Registration Authority (NADRA) designed to answer user queries related to ID card and Passport applications. UMAIR is able to answer user queries related to the domain through discourse with the user by leading the conversation using questions and offering appropriate advice with the intention of leading the discourse to a pre-determined goal. The research and development of UMAIR led to the creation of several novel CA components, namely a new rule based Urdu CA engine which combines pattern matching and sentence/string similarity techniques along with new algorithms to process user utterances. Furthermore, a CA evaluation framework has been researched and tested which addresses the gap in research to develop the evaluation of natural language systems in general. Empirical end user evaluation has validated the new algorithms and components implemented in UMAIR. The results show that UMAIR is effective as an Urdu CA, with the majority of conversations leading to the goal of the conversation. Moreover the results also revealed that the components of the framework work well to mitigate the challenges of free word order and inconsistent word segmentation