We are making progress
to make it easier, faster, smarter.
Less data, More precisely
Natural Language Understanding Engine is a core chatbot technology that provides a response to a request by identifying the user's intent when the user inputs natural language composed of everyday terms as text.
In the case of general rule-based chatbots, the chatbot construction is time-consuming, expensive, and difficult to manage because it requires the user to input as many expressions as possible in advance in order to handle various user expressions.
Xinapse's NLU engine equipped with an inference function designed in a general-specific way based on ambiguous Korean syntax and form. As such, Xinapse's chatbot can accurately identify intents and provide the right answers with relatively less data compared to common rule-based chatbots.
Algorithm rather than Intuition
Xinapse's Topic modeling combines Latent Dirichlet Allocation (LDA) and its own technology to infer the relationship between topics and topics in big data, visualizing these relationships.
In the conventional method of human processing, the analyst has to have expertise in the relevant field. However, through our algorithm, Topic Modeling is possible even without domain-specific knowledge.
Development is promoted based on accurate data rather than intuition. Thus, it allows focus on core services customers require and efficiently utilizes time and resources, eliminating waste.
More Efficient, More Accurate
Companies and institutions are increasingly using unstructured data to overcome the limitations structured data. However, data labeling is not automated, resulting in reduced efficiency and utilization.
Xinapse has achieved significant development in labeling operations of manual handwriting with Auto-labeling using Semi-supervised Learning to label all your data even if it is partially labeled. As such, the efficiency and accuracy of unstructured big data analytics is substantially improved.
Auto-labeling, which automates the preprocessing of big data, can dramatically reduce errors, time, and costs caused by manual handwriting.
Context between sentences
Sentence embedding is a principal technology in the field of natural language processing. The AI learns the meaning of the context and categorizes classes by continuously learning the semantic relations between sentences.
Xinapse uses Facenet technology to classify the nature of document data and calculate the sentences as coordinates, allowing artificial intelligence to recognize them. In addition, the triplet loss technology is used to calculate angles and distances between coordinates, while artificial intelligence classifies data classes by continuously adjusting angles and distances between coordinates through iterative learning.
What do you want to know?
MRC is an AI-based technology in which the AI reads documents like humans, understands the context of a given problem in natural language, and actively finds and answers a given question.
In the existing search system, when a user enters a question, all documents containing the keyword in the question are found. Thus, users must read every document to find the answer. MRC, however, can give you an immediate and specific answer to the question.
By applying MRC, you can improve the performance of your existing homepage or groupware to find information efficiently and increase the level of information sharing.
Virtual Voice Samples
Guess who is talking to you
By using Mel Filter Bank algorithm and deep learning to extract the features of speech, it is possible to synthesize natural speech with less time and data.
• Technical enhancement to create higher sound quality content than other technologies
• System optimization speeds up voice generation (Based on the same system, Google's open technology takes around 5 seconds while Xinapse technology takes around 1 second)
• Creates high-quality content without post-processing
• Acquired celebrity voices through partnerships with entertainment companies
• Rapid generation by minimizing pre- and post-processing in terms of voice model generation