Textual content evaluation is turning into a pervasive process in lots of enterprise areas. Machine Studying is the most typical method utilized in textual content evaluation, and is predicated on statistical and mathematical fashions.
Linguistic approaches, that are primarily based on information of language and its construction, are far much less often used. These two approaches are sometimes seen as different or competing approaches.
This view is a significant impediment to the progress of the Huge Knowledge trade, the place textual content is a big % of massive information.
The 2 approaches are certainly complementary and cooperative approaches that correctly mixed present the simplest manner of extracting high-quality insights from massive information.
The misperception that these two approaches compete predominates within the trade. We disagree: machine studying and linguistic approaches can work collectively. In reality, they need to: linguistic approaches are perfect for understanding language and offering it with construction; machine studying can’t perceive this construction however wants it to extract correct insights from textual content information. So every self-discipline has a “candy spot”.
Linguistic Evaluation is in a greater place to research textual content than Machine Studying. On the one hand, Machine Studying sometimes handles textual content in a “naïve” manner, as a flat set of strings (utilizing completely different variations of the classical “bag of phrases” method).
So sentences like “canine bites man” and “man bites canine” look the identical. This poses a limitation on the quantity of content material that Machine Studying can extract.
However, Deep Linguistic Evaluation is predicated on information about language (grammars, ontologies and dictionaries) and it could possibly deal with the construction of language in any respect ranges (morphology, syntax and semantics).
By making an allowance for the construction of language, Deep Linguistic Evaluation understands advanced phenomena like negation (I by no means appreciated it) and conditionality (I’d prefer it if it had been cheaper) precisely, particularly in advanced circumstances the place two sentences have an identical wording however solely completely different meanings (like “I don’t plan to purchase this product” and “if I don’t purchase this product right now I should buy it tomorrow”).
So Deep Linguistic Evaluation is particularly designed to seek out the construction in (apparently) unstructured textual content.
Nonetheless, Machine Studying is in a greater place to extract insights (from beforehand analyzed and structured textual content, slightly than unstructured), whereas Linguistics has nothing to do with perception extraction.
And we are able to benefit from these two details if we do issues in the appropriate order.
- First, Deep Linguistic Evaluation generates a wealthy and correct illustration of the construction of texts;
- second, Machine Studying makes use of this construction to extract insights from precise options, which is the duty that it naturally excels at.
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