Neural Keyphrase Extraction Using Stacked LSTM and CNN Models
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Solution Overview
Problem
Existing keyphrase extraction methods are often dataset, document type, and language-dependent, making them inefficient for processing large volumes of diverse documents in hosted computing services, which require significant compute resources or human intervention to determine salient features.
Innovation Solution
A keyphrase extraction service using stacked neural networks, specifically bidirectional long short-term memory (LSTM) and convolutional neural network (CNN) models, analyzes character and word-level features independently of datasets, document types, and languages to identify keyphrases, enabling efficient extraction across various contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyphrase extraction methods are used, then extraction accuracy for specific datasets can be achieved, but the methods become dependent on specific datasets, document types, and languages, reducing versatility
Solution Approach 1:
The patent implements a universal keyphrase extraction system using stacked neural networks that can process multiple languages (English, Spanish, French, German, Italian, Portuguese) and various document types simultaneously. The system uses language-agnostic feature extraction at the character and word levels, enabling it to adapt to different datasets and languages without requiring dataset-specific customization, thus achieving both high accuracy and broad versatility.
2Measurement precision
If significant compute resources are allocated to determine salient features, then extraction accuracy improves, but processing time and computational cost increase
Solution Approach 1:
The patent segments the keyphrase extraction process into distinct hierarchical stages: character-level feature extraction, word-level feature extraction, and keyphrase identification. This segmentation allows the system to process features in manageable increments, reducing the computational burden at each stage while maintaining overall accuracy. The stacked neural network architecture processes features sequentially through multiple layers, enabling efficient computation without requiring excessive resources at any single point.
Solution Approach 2:
The system performs preliminary feature extraction at the character and word levels before conducting the main keyphrase identification task. By pre-processing and extracting relevant features in advance through the stacked neural networks, the system reduces the complexity of the subsequent keyphrase selection process, thereby decreasing overall processing time while preserving extraction accuracy.
3Measurement precision
If human intervention is used to determine salient features, then extraction quality improves, but automation level decreases and processing scalability is limited
Solution Approach 1:
The patent implements a self-service automated system where stacked neural networks automatically perform feature extraction and keyphrase identification without human intervention. The system uses unsupervised learning mechanisms to autonomously determine salient features by analyzing character and word-level patterns, eliminating the need for manual curation or human expert involvement while maintaining high extraction quality and enabling scalable processing of large document volumes.
Solution Approach 2:
The patent replaces manual human analysis with automated neural network-based mechanical processing. The stacked neural networks simulate human-like feature extraction and keyphrase identification capabilities through computational algorithms, substituting the mechanical process of human reading and analysis with an automated digital system that can process documents at scale without sacrificing extraction quality.
4Measurement precision
If dataset-specific methods are used, then extraction accuracy for that dataset improves, but the system cannot be efficiently applied to diverse documents of various types and languages
Solution Approach 1:
The patent creates a universal keyphrase extraction system that maintains high productivity across diverse documents by using language-independent character and word level feature extraction. The stacked neural networks are designed to process multiple languages and document types through a unified architecture, eliminating the need for separate dataset-specific processing pipelines and enabling efficient handling of heterogeneous document collections.
Data Source
AI summary
A keyphrase extraction service implements techniques for determining a set of keyphrases associated with set of words. A word is selected from the set of words and a neural model is used to determine a label for the word based on features of the word and labels corresponding to other words of the set of words. The set of keyphrases is determined from the labels associated with the set of words.