We build cutting-edge software that automates the processing and understanding of natural language data.
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Titan NLP is an integrated toolset for processing unstructured information. It uses a new approach to NLP based on combining transformers with knowledge graphs.
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Titan provides more dimensions for text processing. It also delivers higher accuracy than most industry-standard products.
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NLP Developers and Data Scientists
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We set out to design an autonomous system for developing intelligent agents. Titan NLP is the first product suite based on this new cognitive architecture.
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Machine learning, cognitive architectures, knowledge graphs, Natural language processing, knowledge representation and reasoning.
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Early days yet, but Titan NLP is currently been piloted in the defense and space industry.
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Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 1 year ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is 貴方がこれまでに得てきた力、存分に発揮してくださいね。) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: over 1 year ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: almost 2 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: over 2 years ago
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: almost 3 years ago
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