The approach adopted by the Deskin team consisted in searching for similar words! not just identical ones! by setting parameters for the automatic processing model. Machine learning is used to process the entire history on the forum concerned in order to learn the representations of each of the worlds according to the context they appear in. “This technique of word embedding finds similar word meanings based on contextual comparisons!” says Géraldine. “One of the benefits is that the model is indifferent to local mistakes. The word “babys” spelled with a “y” ! for example! will be identified as “babies” thanks to the other items on either side of it.”
What kind of context will derive most benefit from this solution?
A first natural application concerns the Orange forums! the customer care service! and self-troubleshooting. A prototype is being developed on the Orange forums assistance base by identifying the right paradigms for separate your clients precisely dedicated model. Looking ahead! the field is far bigger! because the tool can operate as a sort of semantic search engine.
For example! it is possible to search for very precise information in technical documents! carry out biomedical stop factors when choosing a contractor (on tests! reports! diagnostics! and on any patient or physician records)! produce aids for data-journalism! both for readers looking for information and authors carrying out fact-checking.
And further ahead! another target will be achieved with the analysis of syntax! textual structure! predicates and arguments. “With our current approach! the sentence “Peter repaired Paul’s car” will help us to understand that this is about auto repairs! and that two people are concerned! but european union email list knowing exactly who actually helped who. By identifying the semantic roles of the constituent parts of the sentence! our team is trying to improve detailed understanding of texts!” added the two researchers.
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