Neural Network PoI Identification via Word Embeddings and POS Tags
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Solution Overview
Problem
Current intelligent systems using AI and NLP are inadequate in identifying specific Places of Interest (PoI) from user queries, as they rely on generic approaches and fail to accurately interpret user intent, especially in domain-specific contexts, leading to incomplete extraction of details like time and location.
Innovation Solution
A method and system that utilize word embedding representations, Part-of-Speech tagging, and dependency labeling to identify PoI in natural language inputs, employing a neural network classifier trained on diverse natural language samples to dynamically tag and extract PoI from user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing NLP tools use Named Entity Recognizer (NER) to identify locations, then the system can detect some locations, but it fails to accurately recognize Places of Interest (PoI) because NER relies on uppercase character criteria which do not hold for varied PoI patterns
Solution Approach 1:
The patent transforms the PoI identification problem from rule-based uppercase detection to a machine learning classification problem. It changes the parameters from simple character case detection to multi-feature analysis including word embeddings, POS tags, and dependency labels, enabling accurate recognition of PoI in various patterns and forms.
Solution Approach 2:
The patent replaces the mechanical NER system with fixed rules (uppercase detection) with a neural network-based intelligent system. This substitution enables the system to learn and adapt to various PoI patterns dynamically, overcoming the rigidity of rule-based approaches.
2Productivity
If intelligent systems use generic NLP approaches to decipher user intent, then the system can identify general user intent, but it fails to extract specific granular information such as time and particular Places of Interest in domain-specific contexts
Solution Approach 1:
The patent segments the user query into individual words and analyzes each word's features separately (word embedding, POS tag, dependency label). This segmentation enables the system to identify PoI at the word level, extracting specific granular information that would be lost in generic intent analysis.
Solution Approach 2:
The patent adds multiple dimensions to the analysis by incorporating word embeddings (semantic dimension), POS tags (grammatical dimension), and dependency labels (structural dimension). This multi-dimensional approach enables comprehensive extraction of specific information beyond generic intent recognition.
3Reliability
If the system uses simple NLP rules to identify locations, then the system maintains low complexity, but it achieves poor reliability in recognizing PoI across diverse natural language inputs
Solution Approach 1:
The patent creates a universal PoI identification system that handles diverse PoI patterns (restaurants, hotels, landmarks, addresses, etc.) through a single neural network classifier. This multi-functional approach improves reliability across different domains while maintaining a unified system architecture.
Solution Approach 2:
The patent performs preliminary processing of the input text by generating word embeddings, POS tags, and dependency labels before classification. This preliminary action prepares the data in a standardized format, enabling the neural network to focus on classification and improving overall reliability without excessive complexity.
Data Source
AI summary
Disclosed herein is method and system for identifying one or more Places of Interest (PoI) in a natural language system. Word embedding representation for each word in the natural language input are retrieved from a knowledge repository. Further, for each word, Part-of-Speech (POS) tags are tagged, and dependency labels are generated. Subsequently, a PoI tag is assigned to each word based on the word embedding representation, the POS and the dependency labels of each word. Finally, the one or more PoI are identified based on PoI tag assigned to each word. In an embodiment, the method of present disclosure helps in dynamically identifying one or more PoI from natural language text utterances in interactive systems, thereby enhancing usability of interaction based intelligent systems.