An analysis of machine translation errors on the effectiveness of an Arabic-English QA system

The aim of this paper is to investigate how much the effectiveness of a Question Answering (QA) system was affected by the performance of Machine Translation (MT) based question translation. Nearly 200 questions were selected from TREC QA tracks and ran through a question answering system. It was able to answer 42.6% of the questions correctly in a monolingual run. These questions were then translated manually from English into Arabic and back into English using an MT system, and then re-applied to the QA system. The system was able to answer 10.2% of the translated questions. An analysis of what sort of translation error affected which questions was conducted, concluding that factoid type questions are less prone to translation error than others

A discourse-based approach for Arabic question answering

The treatment of complex questions with explanatory answers involves searching for arguments in texts. Because of the prominent role that discourse relations play in reflecting text-producers’ intentions, capturing the underlying structure of text constitutes a good instructor in this issue. From our extensive review, a system for automatic discourse analysis that creates full rhetorical structures in large scale Arabic texts is currently unavailable. This is due to the high computational complexity involved in processing a large number of hypothesized relations associated with large texts. Therefore, more practical approaches should be investigated. This paper presents a new Arabic Text Parser oriented for question answering systems dealing with لماذا “why” and كيف “how to” questions. The Text Parser presented here considers the sentence as the basic unit of text and incorporates a set of heuristics to avoid computational explosion. With this approach, the developed question answering system reached a significant improvement over the baseline with a Recall of 68% and MRR of 0.62

Systematic Literature Review on Ontology-based Indonesian Question Answering System

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for accurate responses. Secondly, template-based SPARQL query construction, commonly used in Indonesian QA systems, suffers from semantic gaps and inflexibility. Advanced techniques like semantic matching algorithms and dynamic template generation can bridge these gaps and adapt to evolving ontologies. Thirdly, lexical gaps and ambiguity hinder QA systems. Bridging vocabulary mismatches between user queries and ontology labels remains a challenge. Strategies like synonym expansion, word embedding, and ontology enrichment must be explored further to overcome these challenges. Lastly, the review discusses the potential of developing multi-domain ontologies to broaden the knowledge coverage of QA systems. While this presents complex linguistic and ontological challenges, it offers the advantage of responding to various user queries across various domains. This literature review identifies crucial challenges in developing ontology-based Indonesian QA systems and suggests innovative approaches to address these challenges

Islamic Applications of Automatic Question-Answering

A search engine aims to retrieve full documents whereas a question answering system aims to extract the exact answer. A question answering system involves the process of accepting a NL (Natural Language) question, analyzing, and processing to match against a knowledge base to generate the right answer from documents for users. For the Holy Quran this involves accepting the NL question and processing it to retrieve the right verse or verses from our Quran knowledge base. Question answering systems can use two types of algorithms: rule based techniques and/or AI (Artificial Intelligence) based techniques. Question Answering systems have three main components: question classification, information retrieval and answer extraction. We present a rule-based system for the Holy Quran that retrieves the right verse(s) from the Holy Quran instead of generating NL answers. We use a java program to extract the answer from a MS-Access database which contains our knowledge base for our Quran question answering system. We find that the system gives better results for the question after improving the system by removing stop words