Augmenting Content Retrieval Using NLP in AIML
Kapil Shrivastava, Angeles Quezada, Karuna Sharma, Bogart Yail Márquez, Nitish Vashishth · 2024
The paper Augmenting Content Retrieval Using NLP in AIML describes using Natural Language Processing (NLP) with Artificial Intelligence Markup Language (AIML) to enhance content retrieval. Among Markup languages, AIML is most commonly used in chatbots or conversational UI. On the other hand, NLP is a subfield of artificial intelligence that handles reading and understanding human language. Their abstract underscores the importance of improved content extraction for AIML systems that rely on a keyword-based approach, which wouldn't work very well as it fails to capture all elements in human language. Here, wide-scale use of the NLP techniques provides a breakthrough in the analysis. It allows understanding the meaning inherent in natural language and interpreting semanticity through context-sensitive, syntactic rule-based arrangement. The paper proposes a mix of NLP and AIML for better retrieval. Approach: In this technique, we first preprocess the user input using NLP techniques, i.e., POS tagging & Named Entity Recognition, to extract context-specific Keywords/Concepts. Then, these keywords and concepts are mapped with answers in the AIML-based system. The abstract also presents the experimental evaluation of the proposed strategy, which significantly outperforms conventional keyword-based retrieval for content. This proves that applying NLP in AIML systems can even increase the capability to understand and recover user inputs.