“Semantic Role Labeling is the task of determining the latent predicate argument structure of a sentence and providing representations that can that can answer basic questions about sentence meaning, including who did what to whom, etc.” Another way to put this is after we have pre-processed the textual data through parsing, analysis, tokenizing and scans over the inverted indexes we can look for similar terms, sentences and paragraphs that are similar based on the calculated degree of linguistic similarity between the query terms and matching terms in the index. 

From this, you can proceed to use this matching in several ways. One way is to extract key passages that best summarize a result. The passage with the highest similarity scores. If the search is a question – and answers are required to be returned, we can include a text passage that best matches the question. 

Of course, it is not like a human being. For any answer, existing text must be used. The models do not make new sentences or phrases like a human does. Nor can it follow the logic to arrive at conclusions.

How might you use this? You can match very long descriptions or sentences and find products that more closely match the intent of the user. The long natural query we highlighted above might bring a pair of loose fit dark color jeans. It may not exactly match the keywords but it gets us to a more intuitive sense of what a customer is looking for. And this is important to note. Semantic search is a newer technology so we should set our expectations about what it can and cannot do.  We can definitely find matches that are semantically closer to the intent of original query and can match answers to very carefully defined questions. Asking for the trendiest skirt will not return a result if there are no skirts that are defined as trendy! Additionally, semantic search cannot correlate or infer information from different content. If you ask for a bracelet popular in Monte Carlo, nothing will be returned if no bracelets on your site are mentioned as being used in Monte Carlo. 

There are interesting additions to remove some of these limitations. Delvify has implemented a very clever addition to our set called synonym matching. By creating a map of similar words and phrases, we can infer what a customer might want even if that word is not in the descriptions. This is one way Delvify is taking existing AI and moving it forward to the next level.