Labeled Short Text Sequence Generation via Ontology Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Classifying or clustering short text sequences is challenging due to their brevity, making it difficult to compute similarity and determine topics, especially in applications like sentiment analysis and information extraction, where there is a lack of training data and human reviewers are required for labeling.
Innovation Solution
A method and system that automatically generate labeled short text sequences by extracting sentences from documents, grouping them into clusters based on vector representations, determining ontological entities, and labeling them, allowing for model training without human input, enabling easy adaptation to different domains and industries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional clustering methods are used on short text sequences, then computational complexity increases due to difficulty in computing similarity, but classification accuracy remains poor
Solution Approach 1:
The patent introduces sentence embeddings as an intermediary representation that transforms short text sequences into dense vector representations. These embeddings serve as a mediator between the original text and the clustering algorithm, enabling meaningful similarity computation through vector operations rather than direct text comparison, thus resolving the contradiction between measurement precision and computational complexity
Solution Approach 2:
The patent changes the parameter space by transforming text data into embedding vectors with specific dimensional properties. By adjusting embedding dimensions and using appropriate similarity metrics (cosine similarity, Euclidean distance) on these transformed parameters, the system achieves accurate similarity measurement without the combinatorial complexity of traditional text-based approaches
2Measurement precision
If human reviewers are used to label short text sequences, then labeling accuracy improves, but time consumption and cost increase significantly
Solution Approach 1:
The patent implements self-service labeling by enabling the system to automatically generate labels through unsupervised clustering and embedding-based representation. The model serves itself by creating training data without external human intervention, thus achieving accurate labeling while eliminating the time loss associated with manual review processes
Solution Approach 2:
The patent performs preliminary action by pre-processing text into embeddings and pre-clustering sentences before final labeling. This preliminary transformation into a structured representation enables automated label generation that maintains accuracy while dramatically reducing the time required compared to direct human labeling of raw text
3Reliability
If more training data is collected for model training, then model performance improves, but data processing complexity and storage requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the training process into distinct stages: sentence embedding generation, clustering, label assignment, and model training. Each stage processes a specific portion of the data in a structured manner, improving model performance through comprehensive data utilization while managing processing complexity through systematic division of the overall task
4Measurement precision
If domain-specific training data is used, then model accuracy for that domain improves, but adaptability to other domains decreases
Solution Approach 1:
The patent implements universality by creating a domain-agnostic embedding and clustering framework that can process text from any domain. The system uses universal sentence embeddings and clustering algorithms that work across domains, allowing the same pipeline to generate training data for multiple domains without retraining, thus achieving both domain-specific accuracy when needed and broad adaptability
Data Source
AI summary
A set of documents related to a particular topic, industry, or entity are received. Sentences are extract from each document. The sentences are grouped into tuples of one, two, or three consecutive sentences (i.e., short text sequences). The sentence tuples are clustered based on vector representations of the sentences. For each cluster, a set of tuples that best represents or best fits the cluster is selected. These sentence tuples are fed to an ontology to determine ontological entities associated with each tuple. These determined ontological entities are associated with the clusters corresponding to each tuple. The sentence tuples associated with each cluster are labeled based on the ontological entities associated with the cluster. The labeled sentence tuples may then be used for a variety of purposes such as training a model to determine the topic of short text sequences.