论文标题
使用语音识别和命名实体识别从客户对话中处理和提取关键实体
Handling and extracting key entities from customer conversations using Speech recognition and Named Entity recognition
论文作者
论文摘要
在这个现代技术时代，电子商务以快速发展的速度发展，了解客户需求和商业对话细节非常重要。这对于客户保留和满意度至关重要。从这些对话中提取关键的见解非常重要，在开发其产品或解决问题时。了解客户的反馈，响应和产品的重要细节至关重要，它将使用命名实体识别（NER）进行。对于提取实体，我们将使用最佳语音到文本模型将对话转换为文本。该模型将是一个两阶段的网络，其中对话将转换为文本。然后，使用NER BERT变压器模型使用鲁棒技术提取合适的实体。当他们面临的问题时，这将有助于丰富客户体验。如果客户面临问题，他将致电并注册他的投诉。然后，该模型将从此对话中提取关键功能，这是研究问题所必需的。这些功能将包括订单号等细节，以及确切的问题。所有这些将直接从对话中提取，这将减少再次进行对话的努力。
In this modern era of technology with e-commerce developing at a rapid pace, it is very important to understand customer requirements and details from a business conversation. It is very crucial for customer retention and satisfaction. Extracting key insights from these conversations is very important when it comes to developing their product or solving their issue. Understanding customer feedback, responses, and important details of the product are essential and it would be done using Named entity recognition (NER). For extracting the entities we would be converting the conversations to text using the optimal speech-to-text model. The model would be a two-stage network in which the conversation is converted to text. Then, suitable entities are extracted using robust techniques using a NER BERT transformer model. This will aid in the enrichment of customer experience when there is an issue which is faced by them. If a customer faces a problem he will call and register his complaint. The model will then extract the key features from this conversation which will be necessary to look into the problem. These features would include details like the order number, and the exact problem. All these would be extracted directly from the conversation and this would reduce the effort of going through the conversation again.
