Part-of-Speech Tagging with Context Sentences
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional part-of-speech tagging models lack access to context sentences when analyzing a given sentence, leading to difficulties in predicting tags for words with unclear or unfamiliar meanings, as they rely solely on information within the sentence.
Innovation Solution
The proposed method involves determining a set of context sentences based on proximity and using them to inform part-of-speech tagging for target sentences, incorporating techniques like BERT-based models to leverage contextual information from adjacent or semantically adjacent sentences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional part-of-speech tagging models rely solely on information within the sentence, then the model complexity remains low, but the tagging accuracy deteriorates for words with unclear or unfamiliar meanings
Solution Approach 1:
The patent extends the input dimension from single sentence to multiple sentences by incorporating context sentences into the tagging process. The model now processes a sequence of sentences rather than isolated sentences, adding a temporal/dimensional aspect to the input data that provides additional contextual information for ambiguous words.
Solution Approach 2:
The patent performs preliminary processing to identify and select relevant context sentences before the actual tagging operation. By pre-selecting sentences that contain ambiguous words or provide contextual clues, the system prepares the necessary information in advance, reducing the computational burden during the main tagging process.
2Reliability
If context sentences are incorporated into part-of-speech tagging, then the ability to predict tags for ambiguous words improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by selectively processing only certain sentences as context rather than uniformly processing all available text. The system identifies and focuses computational resources on sentences that are most relevant to the target sentence, applying different processing intensity to different parts of the input corpus.
Solution Approach 2:
The patent uses partial action by selecting a limited number of context sentences (e.g., one or two preceding sentences) rather than using all available context. This partial approach provides sufficient contextual information to improve reliability while avoiding the excessive computational cost of processing the entire corpus.
3Loss of information
If multiple context sentences are used for tagging, then the contextual clues available increase, but the data processing complexity increases
Solution Approach 1:
The patent extracts only the necessary contextual information from context sentences rather than processing the entire sentences. By identifying and extracting specific features, words, or patterns that are relevant to tag prediction, the system reduces the amount of data that needs to be processed while retaining the essential contextual clues.
Data Source
AI summary
Computer technology for determining and tagging parts of speech in a text (that is PoS ragging), where the context used by the natural language processing machine logic (for example, NLP software) includes both: (i) other words in the sentence under analysis where a given word to be tagged appears; and (ii) words in the other sentences besides the sentence under analysis. Other context sentences may be selected randomly, by Next Sentence Prediction technology and/or by choosing sentences in textual proximity to the sentence under analysis.