Myanmar News Retrieval Using Kernelized Neural Ranking Model

Myanmar News Retrieval involves searching for and extracting pertinent documents or data depend on a user's query or information need. Neural Information Retrieval (Neural IR) leverages the power of neural networks to enhance the effectiveness of this retrieval process. In a crucial information retrieval (IR) task, neural methods are employed to rank documents in response to a given query. Neural models are utilized in various information retrieval (IR) contexts, including ad-hoc retrieval, recommender systems, multimedia search, and conversational systems aimed at producing responses to natural language queries. The evaluation metrics will be utilized to gauge the effectiveness of the retrieved documents deemed relevant to the given query. This paper introduces a Kernelized Neural Ranking Model (KNRM) designed specifically for ad-hoc retrieval tasks. Through comprehensive analysis of datasets, we analyses that conversational questions exhibit complex phenomena that are absent in current reading comprehension datasets. Our model focuses on adopting a deep architecture specifically at the query term level to support relevance matching.