# Natural Language Processing in Knowledge-Based Support for Operator Assistance
###### Abstract
Consider a scenario where an operator asks a question like, \"How do I fix this?\". The system may not have a predefined response because it lacks a direct match with any known question in its database. Furthermore, the question's vagueness and incompleteness pose additional hurdles to understanding the operator's intent. In these situations, an advanced solution is required. This is where natural language processing (NLP) technology comes into play [15]. NLP empowers assistance systems to comprehend and respond to open questions posed by operators by translating text strings into formal, semantic representations. NLP, in essence, offers a computerized approach to process and understand human language. It serves as a bridge between human language and computer systems, enabling the effective analysis of text or speech [16]. NLP research covers both open and closed domains [17]. NLP projects in both domains are areas of active research and have been extensively studied [18]. Open-domain NLP has received significant attention over the years, focusing on understanding and processing general human language, without specific constraints or limitations to a particular domain. Closed domains are more specialized in different industries and domains where the language and concepts are more specialized. The assembly domain in the assembly environment can be categorized as the closed domain in NLP, which poses a challenge for several reasons. First, to the best of our knowledge, there is limited existing research on the application of NLP techniques in the manufacturing industry, particularly in the area of assembly [19; 20]. It means that there is a lack of publicly available documents and specific corpora related to NLP in the assembly. Secondly, informal writing and poor grammar in procedures like quality reports further add to the challenge. While ChatGPT [21] and other large language models demonstrate proficiency at generating human-like text across diverse fields, their suitability for manufacturing assembly line support requires careful assessment. While these models can offer detailed responses to a wide range of questions, the critical demand for precision in manufacturing means that a single precise answer is often more valuable than extensive information. When the operator interacts with the assistance system, the initial request can be in the form of either textual input in a conversational assistance system or spoken dialogue processed by a speech-to-text system. However, incomplete sentences may occur due to issues such as grammar, spelling, and speech variations, including different accents, dialects, and speaking styles. The assistance system must be capable of handling such incomplete requests effectively. In this research, our objective is to address the challenges associated with NLP in the assembly domain. To tackle the lack of specific corpora related to NLP in assembly, as the first challenge, we aim to design and develop a domain-specific dataset with the dedicated target language 'English' that serves as a benchmark for close-domain analysis. This dataset will capture the unique language patterns, vocabulary, and linguistic characteristics specific to assembly-related tasks. By creating such a dataset, our aim is to provide a valuable resource for future research and contribute to the expansion of NLP applications in the assembly domain. Moreover, we recognize the need for handling incomplete sentences as the second challenge. To address this challenge, our research involves exploring two types of sentences in our designed dataset: declarative and imperative. We consider how both complete and incomplete sentences can be effectively processed within the realm of NLP, employing techniques such as part-of-speech analysis. This exploration is fundamental to bridging the gap between NLP techniques and the assembly domain within manufacturing industry. Furthermore, as part of our work, we have rigorously evaluated three prominent pre-trained NLP libraries: NLTK, SPACY, and Stanford.