DeepNLP
DeepNLP
What is DeepNLP？
With eLAND’s deep learning AI Semantic Analysis technology, enterprises can swiftly extract the key contents from complex and unstructured textual data.
eLAND Information is an iconic company specializing in data intelligence, excelling in AI Semantic Analysis and Search Engine technology. With over 10 years of experience in Chinese language processing, we continuously optimize language models using AI algorithms (Machine Learning). This allows us to automatically extract key sentences or paragraphs from the latest articles, calculate inter-sentence associations, and organize documents. We provide top-tier Semantic Analysis technology for businesses to stay up-to-date.
What is DeepNLP？
With eLAND’s deep learning AI Semantic Analysis technology, enterprises can swiftly extract the key contents from complex and unstructured textual data.
eLAND Information is an iconic company specializing in data intelligence, excelling in AI Semantic Analysis and Search Engine technology. With over 10 years of experience in Chinese language processing, we continuously optimize language models using AI algorithms (Machine Learning). This allows us to automatically extract key sentences or paragraphs from the latest articles, calculate inter-sentence associations, and organize documents. We provide top-tier Semantic Analysis technology for businesses to stay up-to-date.
DeepNLP Operational Architecture
Operational Architecture
The Tornado Semantics Analysis platform includes two key features: DeepNLP Text Miner and DeepNLP Classifier. These are crafted to mimic human cognitive processes for analyzing unstructured text.
Moreover, the platform integrates with non-structured data through the Tornado Search KM System. Leveraging Natural Language Processing (NLP), it extracts essential keywords, analyzes content features and connections, and performs advanced functions like sentiment analysis and automatic classification. The results are then visually presented, allowing users to effortlessly identify crucial information hidden within the extensive dataset.
DeepNLP Highlights
Content feature extraction
Through deep learning, DeepNLP automatically analyzes unstructured textual data to extract feature words related to the main theme of the articles.
Sentiment analysis
Utilizing an AI sentiment model, DeepNLP calculates overall positive, negative, and neutral sentiment scores of the article to understand the conveyed emotional tone.
Content summarization
Simplifying the text content and identifying key words to extract essential sentences or paragraphs, generating a summary of the text.
Feature attribute analysis
Automatically categorizing the extracted feature words based on attributes, such as addresses, names, organizations, etc.
Part-of-speech analysis
Utilizing deep learning technologies to perform “lexical segmentation” and “part-of-speech tagging,” facilitating the subsequent use of qualitative text for quantitative statistics.
DeepNLP
Highlights
Content feature extraction
Through deep learning, DeepNLP automatically analyzes unstructured textual data to extract feature words related to the main theme of the articles.
Sentiment analysis
Utilizing an AI sentiment model, DeepNLP calculates overall positive, negative, and neutral sentiment scores of the article to understand the conveyed emotional tone.
Content summarization
Simplifying the text content and identifying key words to extract essential sentences or paragraphs, generating a summary of the text.
Feature attribute analysis
Automatically categorizing the extracted feature words based on attributes, such as addresses, names, organizations, etc.
Part-of-speech analysis
Utilizing deep learning technologies to perform “lexical segmentation” and “part-of-speech tagging,” facilitating the subsequent use of qualitative text for quantitative statistics.
DeepNLP Applications
Crime Prevention Assistant
Leveraging AI semantic technology to analyze a wide array of investigative data and files, extracting pivotal keywords, annotating information related to individuals, events, locations, and more. This data is presented with the aid of visualization tools, assisting in analyzing cases from a more intuitive and comprehensive perspective, thus minimizing the possibility of missed clues and aiding in criminal investigation operations.
Suggestions for Marketing Strategies
Conducting AI Semantic Analysis on online audience’s browsing behavior to review their website content consumption and subsequently assigning interest tags. This enables the customization of ads and product/marketing content for individuals likely to be interested, thereby boosting click-through rates and, consequently, conversion rates.
Customer Feedback Analysis
Conducting AI Semantic Analysis on public complaints or customer feedback, categorizing them by type, and automatically assigning them to respective responsible units. Additionally, the system can automatically calculate their sentiment scores, extract keywords, and assist in evaluating the situation, forming response strategies and contingency plans.
Local Government Petition Analysis
Conducting AI Semantic Analysis on petition cases to extract key terms, personnel, time, location, and other information. Automatically assigning them to responsible units, summarizing important issues within, and conducting related statistics on petition cases and crucial topics to expedite government processing and strategy formulation.
Enhancing Contents
Utilizing NLP Semantic Analysis technology to replace manual classification, process unstructured data, and expand related concepts based on article content. This enriches reading quality and enhances user engagement.
Creating Knowledge Maps for Enterprises
Empowering enterprises to analyze internal data, summarizing and classifying it based on content, generating summaries, and establishing a dedicated knowledge map. Users can save time on repetitive reading and minimize errors, accessing more complete content and enhancing overall efficiency and making more comprehensive plans.
DeepNLP
Applications
Crime Prevention Assistant
Leveraging semantic technology to analyze a wide array of investigative data and files, extracting pivotal keywords, annotating information related to individuals, events, locations, and more. This data is presented with the aid of visualization tools, assisting in analyzing cases from a more intuitive and comprehensive perspective, thus minimizing the possibility of missed clues and aiding in criminal investigation operations.
Suggestions for Marketing Strategies
Conducting AI Semantic Analysis on online audience’s browsing behavior to review their website content consumption and subsequently assigning interest tags. This enables the customization of ads and product/marketing content for individuals likely to be interested, thereby boosting click-through rates and, consequently, conversion rates.
Customer Feedback Analysis
Conducting AI Semantic Analysis on public complaints or customer feedback, categorizing them by type, and automatically assigning them to respective responsible units. Additionally, the system can automatically calculate their sentiment scores, extract keywords, and assist in evaluating the situation, forming response strategies and contingency plans.
Local Government Petition Analysis
Conducting AI Semantic Analysis on petition cases to extract key terms, personnel, time, location, and other information. Automatically assigning them to responsible units, summarizing important issues within, and conducting related statistics on petition cases and crucial topics to expedite government processing and strategy formulation.
Enhancing Contents
Utilizing NLP Semantic Analysis technology to replace manual classification, process unstructured data, and expand related concepts based on article content. This enriches reading quality and enhances user engagement.
Creating Knowledge Maps for Enterprises
Empowering enterprises to analyze internal data, summarizing and classifying it based on content, generating summaries, and establishing a dedicated knowledge map. Users can save time on repetitive reading and minimize errors, accessing more complete content and enhancing overall efficiency and making more comprehensive plans.
DeepNLP Features
DeepNLP
Features
Automatic Extraction
Organizing unstructural data and extracting crucial words, achieving understanding of new terms without the need to create a lexicon.
Association Analysis
Analyzing associations, extending to similar documents, providing a more comprehensive theme recommendation.
Sentiment Analysis
Sentiment Analysis is used to evaluate positive and negative emotions within text, with the flexibility to extend other semantic dimensions.
Automatic Classification
Automatically categorizing articles to enhance processing efficiency, effectively reducing labor costs.
Learning from Feedback
Equipped with the ability to learn from feedback and improve accuracy by using blacklists and whitelists for result correction.
