Gupta et al., 2015 - Google Patents
Deep learning methods for the extraction of relations in natural language textGupta et al., 2015
View PDF- Document ID
- 717044471796934492
- Author
- Gupta P
- Runkler T
- Adel H
- Andrassy B
- Zimmermann H
- Schütze H
- Publication year
- Publication venue
- Master's thesis, Technical University of Munich, Germany
External Links
Snippet
With much human knowledge residing in the text documents and the amount of unstructured information growing constantly on the web, as a result the problem of extracting desired information is getting difficult by the day. There is a need of intelligent systems to …
- 238000000605 extraction 0 title abstract description 12
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2705—Parsing
- G06F17/271—Syntactic parsing, e.g. based on context-free grammar [CFG], unification grammars
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
- G06F17/30657—Query processing
- G06F17/30675—Query execution
- G06F17/30684—Query execution using natural language analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2785—Semantic analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/27—Automatic analysis, e.g. parsing
- G06F17/2765—Recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/28—Processing or translating of natural language
- G06F17/2872—Rule based translation
- G06F17/2881—Natural language generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B19/00—Teaching not covered by other main groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Coulombe | Text data augmentation made simple by leveraging nlp cloud apis | |
Neubig | Neural machine translation and sequence-to-sequence models: A tutorial | |
Paulus et al. | Global belief recursive neural networks | |
Beysolow | Applied natural language processing with python | |
Shaikh et al. | Bloom’s learning outcomes’ automatic classification using lstm and pretrained word embeddings | |
Graff et al. | Evomsa: A multilingual evolutionary approach for sentiment analysis [application notes] | |
Mohapatra et al. | Text classification using NLP based machine learning approach | |
Gupta et al. | Deep learning methods for the extraction of relations in natural language text | |
Aljohani et al. | Learners demographics classification on MOOCs during the COVID-19: author profiling via deep learning based on semantic and syntactic representations | |
Kandi | Language Modelling for Handling Out-of-Vocabulary Words in Natural Language Processing | |
Jia | Building robust natural language processing systems | |
Hitkul et al. | Aspect-based sentiment analysis of financial headlines and microblogs | |
Xiang et al. | A message-passing multi-task architecture for the implicit event and polarity detection | |
Lee et al. | Modeling human mental states with an entity-based narrative graph | |
Weiss et al. | Sense classification of shallow discourse relations with focused RNNs | |
Fernandes | A deep learning approach to named entity recognition in portuguese texts | |
Kulkarni et al. | Evolution of Neural Networks to Large Language Models | |
Zouari | French AXA insurance word embeddings: Effects of fine-tuning bert and camembert on AXA france’s data | |
Arkhangel'skaya et al. | Deep learning for natural language processing: a survey | |
Kardakis | Machine Learning Techniques for Sentiment Analysis and Emotion Recognition in Natural Language | |
Vernikos | Adversarial Fine-Tuning of Pretrained Language Models | |
Zaruba | Using natural language processing to measure the consistency of opinions expressed by politicians | |
Fytili | Aspect Extraction from Greek Product Reviews | |
Adebayo et al. | Textual Inference with Deep Learning Technique | |
Araz | Transformer Neural Networks for Automated Story Generation |