Searching for a fine dining guide by browsing several restaurant reviews in various sites and coming up with a restaurant choice that satisfies your appetite is a time-consuming task. And despite your search, you might end up in the wrong restaurant with aversive food, delayed service or a noisy environment.
Searching with a generic search engine, built up on the keyword based technology is more likely to give such a disastrous result. Most netizens might just prefer to check out from one review site for all the restaurant ratings and might read a lot of fluff and flier; only to visit the restaurant and realize that there was no meat in the writing.
Restaurant search engines like BooRah have been built using the Natural Language Processing technology. They are designed to analyze user-generated content and compile specific scores for food, service and ambience from user sentiments from across the web – blogs, review sites, social networks, wikis.
Natural language processing means the computer-aided processing of language produced by a human. But human language is inherently irregular and the most reliable results are obtained when a human is involved in at least some part of the processing. NLP studies the problems of automated generation and understanding of human languages. Natural-language-generation systems convert information from computer databases into normal-sounding human language. Natural Language Processing is simplifying the existing corpus of human-readable text and simplifying grammar and structure, while retaining the underlying meaning.
So far the search engines have been powered by a keyword based technology, which has been making searches hectic with vast amounts of data mixed with a lot of irrelevant search results. But, the use of natural language interface can make search engines more precise in retrieving information. Since the searches are based on the inherent meaning of the search terms, users can get the most relevant search results. The advanced search techniques with NLP are breaking the confines of keyword search.
The NLP powered restaurant search engine understands the structure and nuances of natural language and hence makes the search more natural and intuitive, thereby delivering higher quality search results. With the web getting more meaning-oriented (Semantic), the semantic crawling technology in latest search engines aggregates the most relevant content from the web. The NLP understands and extracts the most relevant content from plain English text and then correlates and extracts a given domain.
The restaurant guide spruces up the search for good restaurants by capturing the community vibe and social essence of existing online groups, and comparing these results to an individual's search criteria; thereby delivering unsurpassed relevance with the broadest community reach. BooRah combines two elements -voting clicks and analyzed reviews text into a single overall score.
Users can see all the professional and user reviews from across the net in one single site. They can search details on restaurants ratings, menus, photographs, discount restaurant gift certificates and coupons, other relevant restaurant information like online restaurant reservations, home delivery, party or private rooms, addresses, hours, driving directions, maps, etc. for all local restaurants. Since the search engine dwells on meaning, users can find the best reviews first and don’t have to sort the relevant form the irrelevant data as in a generic search engine.
Natural language processing is out to make the restaurant searches more meaningful, with diners getting the perfect cuisine, taste, service and ambiance.
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