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Video Recognition Multilingual Machine Translation Natural Language Processing Speech Recognition Knowledge Graph

Introduction

GTCOM established the 2020 Cognitive Intelligence Research Institute in 2016 through collaboration with the world's foremost universities, research institutions and scientists. Based on the cutting-edge technologies of artificial intelligence and deep learning, such as the convolutional neural network, recurrent neural network, deep-belief network, conditional random fields, random forests and the word-embedding model, combined with hundreds of billions of global multilingual corpora data resources, GTCOM has built a unique algorithm cloud platform and a machine-translation training system.


The cloud platform of the multilingual natural-language processing algorithm brings developers highly stable, efficient cloud services that meet the needs of global information-processing applications. Additionally, the self-developed multilingual neural network machine-translation system supports multilingual, multi-field translation and can be customized according to a given customer's needs, thereby offering effective solutions for next-generation cross-language deep information-processing services.


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Applications

  • Word segmentation and part-of-speech tagging
  • The term "word segmentation" refers to the process of dividing a string in a written language into its component words according to grammatical norms. The term "part-of-speech tagging" refers to the process of defining a word sequence and finding the most
  • Named-entity recognition
  • Named-entity recognition is an important tool for applications such as information extraction, question-answering systems, semantic understanding and machine translation. Thus, it plays a fundamental role in natural-language processing. We employ a statis
  • Sentiment analysis
  • The text sentiment analysis algorithm can automatically analyze and recognize the opinions or attitudes expressed in the articles and provide sentiment tendency indicators that can express the polarity and intensity of sentiments. The sentiment analysi
  • Keyword extraction
  • The keyword-extraction algorithm is used to extract text subjects and help users quickly obtain the desired core content. It integrates a variety of machine-learning methods and a large amount of corpus resources. It currently supports 10 languages: Chine
  • Text summarization
  • The automatic summarization algorithm refers to the process of automatically generating a simple, coherent essay that expresses the core content of the original document. It facilitates efficient compression of the original text and assists users in readi
  • Language recognition
  • The language recognition algorithm refers to the process of automatically determining the language of the input texts.Based on N-Gram and Bayes' theorem, we have developed a set of language recognition technologies that support dozens of languages. The re
  • Text classification
  • The text-classification algorithm refers to the process of automatically marking the text categories according to a classification system or standard. The text-classification algorithm can be used to classify unstructured information according to a given
  • Sensitivity determination
  • The sensitivity-determination algorithm is mainly used to filter sensitive information, including that of a reactionary, pornographic and/or violent nature. Based on the statistical machine-learning model, we've implemented a sensitivity analysis system t
  • Text-quality assessment
  • The text-quality assessment algorithm is used to filter and clean data collected by Internet users, thereby improving information quality and enhancing user experience. It can quickly identify noisy data containing garbled characters, codes and scripts as
  • Event element extraction
  • The event element extraction algorithm can structure unstructured natural-language texts and can be used for in-depth analysis and mining of news events. We use an unsupervised learning method to extract the most important time, place, character and event
  • Multilingual word embedding
  • The term "word embedding" is often used in the field of deep learning. Word embedding expresses not simply the word but also its semantic relationship with others. Word embedding is important as a means for the efficient, quantitative expression of nat

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