Nothing Special   »   [go: up one dir, main page]

Gu et al., 2018 - Google Patents

Language modeling with sparse product of sememe experts

Gu et al., 2018

View PDF
Document ID
12815699110015450250
Author
Gu Y
Yan J
Zhu H
Liu Z
Xie R
Sun M
Lin F
Lin L
Publication year
Publication venue
arXiv preprint arXiv:1810.12387

External Links

Snippet

Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but not necessarily atomic semantic units. Inspired by HowNet, we use sememes, the …
Continue reading at arxiv.org (PDF) (other versions)

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
    • 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
    • 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
    • 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
    • G06F17/30—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
    • 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
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • 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
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines

Similar Documents

Publication Publication Date Title
Gu et al. Language modeling with sparse product of sememe experts
Young et al. Recent trends in deep learning based natural language processing
Wang et al. A survey of word embeddings based on deep learning
US20220245365A1 (en) Translation method and apparatus based on multimodal machine learning, device, and storage medium
Dhingra et al. Embedding text in hyperbolic spaces
Wang et al. Combining knowledge with deep convolutional neural networks for short text classification.
Du et al. An effective sarcasm detection approach based on sentimental context and individual expression habits
Paaß et al. Foundation models for natural language processing: Pre-trained language models integrating media
US10339440B2 (en) Systems and methods for neural language modeling
Dashtipour et al. Exploiting deep learning for Persian sentiment analysis
Zulqarnain et al. An efficient two-state GRU based on feature attention mechanism for sentiment analysis
Ma et al. Extraction of temporal information from social media messages using the BERT model
Wu et al. Multimodal aspect extraction with region-aware alignment network
Bokka et al. Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks
Onan Deep learning based sentiment analysis on product reviews on Twitter
Wu et al. Combining contextual information by self-attention mechanism in convolutional neural networks for text classification
Yan et al. Leveraging contextual sentences for text classification by using a neural attention model
Gou et al. Integrating BERT embeddings and BiLSTM for emotion analysis of dialogue
Li et al. Graph convolutional networks with hierarchical multi-head attention for aspect-level sentiment classification
Zhen et al. The research of convolutional neural network based on integrated classification in question classification
Liu et al. Sentence part-enhanced BERT with respect to downstream tasks
Malhotra et al. Bidirectional transfer learning model for sentiment analysis of natural language
Keivanlou-Shahrestanaki et al. Interpreting sarcasm on social media using attention-based neural networks
Gao et al. Attention-based BiLSTM network with lexical feature for emotion classification
Zhang et al. Chinese-English mixed text normalization