Research On Natural Language Understanding Algorithm For Cloud-Based Service Robots

By leveraging the robust storage and computational resources of cloud service platforms and integrating robots with deep learning techniques for natural language understanding,robots can more accurately identify,analyze,and interpret natural language.This not only propels further research into natural language understanding algorithms but also enhances the level of intelligence in robots.However,due to the diversity and complexity of real-world application scenarios,cloud service robots still face numerous challenges.These challenges include:1)Given the constantly changing complex environment,how to effectively construct and timely update their knowledge systems to ensure a comprehensive understanding of the objective world;2)During humanrobot emotional interactions,how to perceive and recognize fine-grained emotional shifts;3)How to amalgamate multilingual knowledge to achieve cross-lingual information sharing and understanding.All these challenges are closely intertwined with natural language understanding.Consequently,this paper delves into the research of natural language understanding algorithms tailored for cloud service robots,and our innovative contributions are as follows:(1)In response to the challenge faced by cloud service robots that need to swiftly and precisely construct and update their knowledge systems in dynamic complex environments,this paper particularly focuses on the pivotal task of extracting knowledge from a plethora of unstructured data—namely,neural relation extraction.Considering the issues of noisy data and long-tail distribution inherent in this task,we introduce a method based on a graph attention mechanism strategy for neural relation extraction.This allows for the efficient extraction of knowledge,updating the knowledge system of the cloud service robot.Our strategy encompasses two innovative mechanisms:1)Utilizing a fine-grained alignment mechanism based on multi-instance learning strategies,it deeply mines potential relational information within text instances;2)Employing an inductive mechanism based on graph attention strategy,it can derive an enhanced relational latent vector representation.This implicit relational representation then guides the label generation for text instances.Experimental results demonstrate that our proposed strategy,grounded on the graph attention mechanism,can effectively mitigate the interference from noisy data while also alleviating the effects brought by the long-tail distribution.(2)Addressing the challenge of enabling cloud service robots to accurately detect fine-grained emotional nuances in natural language,this study delves into the finegrained sentiment analysis of intricate texts.We propose an emotion classification approach grounded in a multi-layered,fine-grained collaborative attention strategy.This allows for a more precise identification of fine-grained emotional entities and their corresponding sentiments within natural language texts,fostering deeper,personalized interactions between the cloud service robots and humans.Our strategy comprises two innovative mechanisms:1)A word-level one-hop mechanism predominantly examining the sentimental association between sentiment words and their surrounding context at the word level;2)An interaction mechanism at the feature level,considering the sentimental correlation from the perspective of deep semantic feature interactions.Extensive experimental results indicate that the collaborative attention mechanism proposed in this study significantly enhances the model’s accuracy in fine-grained sentiment analysis.(3)In the face of the multi-lingual understanding challenges cloud service robots encounter amidst the tide of globalization,this study addresses the inconsistency issues in cross-lingual representation performance of multilingual pre-trained models.We introduce a "plug-and-play" multi-granularity contrastive adapter mechanism.By leveraging multi-granularity aligned corpora for contrastive learning,we establish a more universal language representation pattern,thereby enhancing the model’s cross-lingual transfer capabilities.The designed adapter is not only streamlined and user-friendly but also seamlessly integrates into the technological framework of cloud service robots.Comprehensive experiments demonstrate that the adapter mechanism proposed in this study effectively bolsters cross-lingual comprehension,enabling the model to accommodate a myriad of languages.