Fully understanding the meaning of a text in fine detail is the Holy Grail of artificial intelligence. In many cases! however! the real need is to understand an utterance roughly in order to process large volumes of data. This low-level processing is enough to help people find the right information! whereas in high-level processing there is a need to reliably understand the full meaning of any text extract.
Online forums have proved to be highly valuable
Rich in information. In them we can see a onversions by offering products and/or services human collective intelligence at work where some people come along with problems and others with solutions. But forum content is as yet under-exploited. Starting with knowledge bases! it is possible to automatically answer questions of the “Who”! “What” or “How many” type! such as “How tall is the Eiffel Tower?” or “Who assassinated Abraham Lincoln?” Conversely! it is far more difficult to answer “Why” or “How” questions.
“The “question & answer” paradigm is important in the field of artificial intelligence. It is even! in a sense! core to it: I ask a question and a smart machine gives me the answer. Our approach is different: when we ask a question! we try to identify all the similar pricing on marketplaces: how to calculate the price of a product that have already been asked and to flag up all the (human) answers that have already been given!” explains Géraldine.
Calculating semantic similarity
For the past decade! the annual SemEval international competitions involved large numbers of european union email list from all over the word! working on a variety of semantic analysis tasks. During the SemEval 2017 campaign! a “Community Question Answering” task precisely tackled the problem of identifying similar questions in forums. When asking a question on a pre-defined corpus! Google displayed the ten best results.
The challenge was to improve on Google! The campaign test data concerned an English-language forum for western expats in Qatar! dealing with all sorts of everyday life topics (where to find the best restaurant! how to hire a child minder! which is the best bank! etc.). “Our team won the competition with a robust solution able to calculate the semantic similarity between words! even in data that was “infected” by spelling or grammar mistakes!” says Delphine.