Multi-Word Phrase Extraction Using Hash Tables and Suffix Tries
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Solution Overview
Problem
Existing string matching algorithms are inefficient for extracting specific information like movie titles from human language text, as they work at the character level and are limited to specific languages, requiring a large number of comparisons and being unsuitable for handling the complexity of human language.
Innovation Solution
The system employs multi-word pattern matching at the word level, utilizing Zipf's law to reduce comparisons and hash tables for efficient comparison, allowing for multiple language support and handling large element sets, with preprocessing techniques like suffix trie trees and hash tables to index and match phrases efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard string matching algorithms (Knuth-Pratt-Morris, Boyer-Moore, Aho-Corasick) are used to match patterns in text, then the algorithms can match strings efficiently, but they work at character level, are limited to specific languages, and require a large number of comparisons making them inefficient for extracting information from human language text
Solution Approach 1:
The patent segments the text into words rather than processing at the character level. By dividing the input text into discrete word units and using word-level patterns, the system reduces the complexity of matching and decreases the number of comparisons needed, directly addressing the inefficiency of character-level algorithms for human language text
Solution Approach 2:
The patent changes the fundamental parameter of matching from character-level to word-level. This parameter change enables the system to leverage linguistic properties of words (such as frequency distributions and semantic relationships) to reduce the number of comparisons and improve extraction efficiency across multiple languages
2Adaptability or versatility
If character-level string matching is used, then the algorithms can be pre-computed for each alphabet element, but the algorithms are limited to specific languages and cannot effectively handle the complexity of human language text
Solution Approach 1:
The patent implements a universal word-level matching framework that can handle multiple languages simultaneously. By operating at the word level rather than character level, the system can accommodate the vocabulary and grammatical structures of different languages without requiring language-specific pre-computation, achieving multi-language adaptability
Solution Approach 2:
The patent uses hash tables to create efficient lookups of word patterns and their occurrences. This copying mechanism allows the system to store and retrieve word frequency information and pattern matches rapidly, reducing the computational complexity of multi-language text processing
3Measurement precision
If a large number of string comparisons are performed to extract information, then more accurate extraction can be achieved, but the algorithms become inefficient and time-consuming
Solution Approach 1:
The patent performs preliminary actions by pre-processing the text into words and pre-computing word frequency statistics and pattern occurrences. This preliminary organization of data allows the system to quickly identify and extract accurate phrases without performing numerous comparisons during the actual extraction process, thereby maintaining high accuracy while improving processing speed
Data Source
AI summary
Systems and methods for extracting phrases from text are disclosed. In an exemplary embodiment, a method may include preprocessing desired phrases into at least one phrase indexing data structure for efficient matching. The method may also include scanning text to construct a hash table including keys and corresponding entries. The method may also include locating suffix trie trees for each word in the hash table. The method may also include matching each position in the hash table against the suffix trie trees, and outputting phrases matched in the scanned text.