Document Segment Embeddings for Accurate Screenplay Attribute Extraction
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Solution Overview
Problem
Extraction of metadata from complex screenplays is difficult and resource-intensive, often involving bias and requiring extensive computational resources.
Innovation Solution
A system that utilizes clustering and inverse document frequency weighting to generate embeddings for document segments, followed by classification to extract attributes such as genre, plot, and mood, leveraging pre-trained language models to reduce computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional metadata extraction methods are used from complex screenplays, then comprehensive content attributes can be obtained, but the process becomes resource-intensive and computationally expensive
Solution Approach 1:
The screenplay is divided into multiple segments, and embeddings are generated for each segment individually. This segmentation allows the system to process large documents in manageable chunks, reducing the computational burden while maintaining comprehensive attribute extraction across the entire document.
Solution Approach 2:
The patent introduces embeddings as an intermediary representation between the raw screenplay text and the final metadata extraction. These embeddings capture the semantic meaning of text segments in a compressed vector form, enabling more efficient processing while preserving the essential information needed for accurate attribute classification.
2Measurement precision
If traditional metadata extraction methods are used from complex screenplays, then comprehensive content attributes can be obtained, but the process becomes complex and time-consuming
Solution Approach 1:
The system performs preliminary action by generating embeddings for text segments before the actual metadata extraction and classification processes. This pre-processing step transforms the raw text into a more suitable representation format, making subsequent classification operations faster and more efficient.
Solution Approach 2:
The patent changes the parameter representation by converting text segments into embedding vectors with specific dimensionalities. This parameter transformation enables the use of efficient vector-based classification algorithms that are significantly faster than traditional text processing methods while maintaining or improving classification accuracy.
3Measurement precision
If traditional metadata extraction methods are used, then content attributes can be extracted, but human bias is introduced into the process
Solution Approach 1:
The patent replaces the mechanical system of human metadata extraction with an automated computational system based on embeddings and machine learning classifiers. This substitution eliminates human bias from the extraction process while maintaining high accuracy through learned patterns from training data, making the system more objective and consistent.
Data Source
AI summary
A method includes determining embeddings in an embedding space for segments of a plurality of documents. A cluster is determined for respective segments based on a set of clusters. The cluster is determined based on a position of respective embeddings in the embedding space. The method determines a weight for the cluster for respective embeddings. The respective embeddings are weighted for a document in the plurality of documents using the weight of the cluster for the respective embeddings to generate weighted embeddings. A set of attributes from the weighted embeddings is determined for the document.