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 Community question answering platforms and crowd-based question answering forums provide us with the dais to post questions online, also answer. This helps all the users to get desired answers from a set of expert reviewer users. For instance, for a person with mobility needs, going out into the world means facing a lot of challenges. This explains clearly why we need accessibility information and why Google Maps shows us information about the same. It not only gives us basic information about the place but also tells us if the place we are going to visit has elevators, stair-free entrances and also if wheelchair facilities are available. Taking this into account, people have included wheelchair accessibility details to a million places. But in the long run, how is this going to make an impact? Local Guides can be used to share our knowledge on Google Maps. It has been estimated that if every Local Guide answers three questions every day for two weeks, then we can have about two billion answers to help everyone navigate. Therefore question generation is a key challenge that we face with regard to natural languages. The potential benefit of employing automated systems for generating questions can help minimize the dependency on humans to generate questions but will instead use a system which automates the generation of questions from the available data such as users visited locations, already reviewed places, likes, interests and user experience. The main objective is to gain an insight into the working of online crowd based review and question answering systems. In this paper, we have implemented a Part of Speech (POS) tagger, Text-to-Question generation task using syntactic analysis and a Named Entity extraction. This not only allows us to identify potential questions for users but also generates new questions. This concentrates on three major aspects namely: expert learning, best answer detection, and similar question retrieval. Generally, user profiles and user topics can be learned from previously answered questions. We can generate questions based on the subject, verb, object, and preposition using predefined interaction rules. Using question banks with all possible questions with the corresponding tags we can generate review questions by mapping the tags to user location details. This makes it easier to retrieve the appropriate questions and present them to the user for reviews. This can also facilitate a number of applications. This allows us to ask more questions to frequent reviewers. It also allows us to ask similar questions to people with similar tastes. Based on a person’s reviews on a particular location we can not only ask them more questions about the area but also suggest locations in the same locality and locations under the same criteria. Shortest routes to frequently visited areas can be suggested. More questions can be added from the various user reviews in order to get multiple suggestions on an opinion. This can be done by retrieving user typed reviews and generating questions from sentences. The sentence being a complex one should first be converted to an elementary sentence using a syntactic parser. The needed information can be encoded using a Part of Speech tagger and a Named Entity Recogniser. Based on these tags we can determine the types of questions that can possibly be generated from the sentences. The questions along with the corresponding tags are then added to the database. This system should be intelligent enough to learn about user interest and user topics from previous reviews and questions answered. The main aspect of generating a question is the goal of the question and its importance. It will be difficult to understand whether the posed question is good without knowing its context. Another important idea is aimed at attracting as many participants as possible and also promoting a fair comparison environment. Local Guides is a global community of explorers who write reviews, share photos, answer questions, add or edit places, and check facts on Google Maps. Millions of people rely on contributions like these to decide where to go and what to do. Every place a user reviews, photographs, adds, edits, or provides additional info for on Google Maps earns the reviewer points. When current location services are active on the user’s device, Google tracks the places he visits and asks him questions about the place and several other neighbouring places. The user reviews given by the user can not only be used to help other users but also to generate more questions and verify the correctness of the reviews obtained previously. As an incentive, the user can earn points for every review or question answered or even for uploading pictures. These points can earn the reviewer badges which is an attestation for their contributions being reliable and useful. This paper talks about the possible idea behind Local Guides and other such Community based answering systems. In an automated environment, users prefer intelligent systems which are able to generate their own questions rather than having to manually frame questions to the users. The system will have a question bank initially and will keep updating it from time to time as it learns from user reviews and other frequently asked questions. Since this system can be vulnerable to fake edits, copied or stolen photos, off-topic answers, defamatory language, personal attacks,

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