NLP Model Training with IR Annotations for Search Queries
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Solution Overview
Problem
Natural language processing models face difficulties in interpreting search queries due to their short, grammatically unsound, and context-lacking nature, which reduces their effectiveness in identifying part-of-speech and syntactic structure, especially when trained on well-behaved domains like news reports.
Innovation Solution
Incorporating information retrieval model annotations into the training data set to enhance part-of-speech tagging and syntactic parsing, linking text to knowledge graphs, concept graphs, and entity repositories, and using these annotations to train natural language processing models for improved predictions on search queries and results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a natural language processing model is trained on well-behaved domains like news reports, then the model achieves good performance on structured text, but the model performs poorly on search queries which are short, grammatically unsound, and lacking in context
Solution Approach 1:
The patent applies preliminary action by pre-processing search queries with an information retrieval model to generate annotations (part-of-speech tags, syntactic structures, entity mentions) before feeding them to the NLP model. This pre-annotation process prepares the raw search query data in advance, transforming it into a format that resembles well-behaved training data, thereby enabling the model to handle search queries effectively without requiring extensive retraining on query-specific data
Solution Approach 2:
The patent introduces an information retrieval model as an intermediary component that sits between the search query input and the NLP model. This intermediary generates preliminary linguistic annotations and structures that bridge the gap between the unstructured search query and the NLP model's expectations, allowing the model to process queries it was not originally designed for while maintaining its core capabilities on structured text
2Productivity
If training examples are used without additional annotations, then the training process is simpler and faster, but the model's ability to disambiguate syntactically confusable labels and identify syntactic structure is reduced
Solution Approach 1:
The patent applies preliminary action by pre-computing detailed linguistic annotations (part-of-speech tags, syntactic parses, entity mentions) using an information retrieval model before training the NLP model. These pre-generated annotations enrich the training examples with additional linguistic information that would otherwise require complex and time-consuming manual annotation processes, thereby improving training accuracy without proportionally increasing training time
Solution Approach 2:
The patent uses copying by generating synthetic training annotations through the information retrieval model that mimic the structure and quality of manually annotated training data. Instead of requiring expensive and time-consuming human annotation for each training example, the system copies the annotation process through automated model generation, creating high-quality training data at scale while maintaining training efficiency
Data Source
AI summary
Systems and techniques are provided for training a natural language processing model with information retrieval model annotations. A natural language processing model may be trained, through machine learning, using training examples that include part-of-speech tagging and annotations added by an information retrieval model. The natural language processing model may generate part-of-speech, parse-tree, beginning, inside, and outside label, mention chunking, and named-entity recognition predictions with confidence scores for text in the training examples. The information retrieval model annotations and part-of-speech tagging in the training example may be used to determine the accuracy of the predictions, and the natural language processing model may be adjusted. After training, the natural language processing model may be used to make predictions for novel input, such as search queries and potential search results. The search queries and potential search results may have information retrieval model annotations.