“Sidhhanth is a good, intelligent, smartworking, problem solver, creative person having a keen desire to go into the depth of the problem and solve it. All the best!”
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You can now get a maid for just ₹49/hour, in 10 minutes... Thanks to Snabbit, a startup launched in 2024 that’s quietly solving one of India’s…
You can now get a maid for just ₹49/hour, in 10 minutes... Thanks to Snabbit, a startup launched in 2024 that’s quietly solving one of India’s…
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It's official! Google Play updated their policy to allow developers to use alternative DTC payment flows in their U.S. Android apps. There's no…
It's official! Google Play updated their policy to allow developers to use alternative DTC payment flows in their U.S. Android apps. There's no…
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𝗟𝗼𝘄-𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗡𝗟𝗣 𝗽𝗶𝘁𝗳𝗮𝗹𝗹𝘀 & 𝘁𝗿𝗶𝗰𝗸𝘀 — 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝗛𝗶𝗻𝗱𝗶 𝗲𝗺𝗼𝘁𝗶𝗼𝗻 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Building AI…
𝗟𝗼𝘄-𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗡𝗟𝗣 𝗽𝗶𝘁𝗳𝗮𝗹𝗹𝘀 & 𝘁𝗿𝗶𝗰𝗸𝘀 — 𝗹𝗲𝘀𝘀𝗼𝗻𝘀 𝗳𝗿𝗼𝗺 𝗛𝗶𝗻𝗱𝗶 𝗲𝗺𝗼𝘁𝗶𝗼𝗻 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Building AI…
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Experience & Education
Publications
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Joint Bootstrapping of Corpus Annotations and Types
Empirical Methods on Natural Language Processing, Seattle
Web search can be enhanced in powerful ways if token spans in Web text are annotated with disambiguated entities from large catalogs like Freebase. Entity annotators need to be trained on sample mention snippets. Wikipedia entities and annotated pages offer high-quality labeled data for training and evaluation. Unfortunately, Wikipedia features only one-ninth the number of entities as Freebase, and these are a highly biased sample of well-connected, frequently mentioned ``head'' entities.…
Web search can be enhanced in powerful ways if token spans in Web text are annotated with disambiguated entities from large catalogs like Freebase. Entity annotators need to be trained on sample mention snippets. Wikipedia entities and annotated pages offer high-quality labeled data for training and evaluation. Unfortunately, Wikipedia features only one-ninth the number of entities as Freebase, and these are a highly biased sample of well-connected, frequently mentioned ``head'' entities. To bring hope to ``tail'' entities, we broaden our goal to a second task: assigning Wikipedia types to entities in Freebase but not Wikipedia. The two tasks are synergistic: knowing the types
of unfamiliar entities helps disambiguate mentions, and words in mention contexts help assign types to entities. We present TMI, a bipartite graphical model for joint type-mention inference. TMI attempts no schema integration or entity resolution, but exploits the above-mentioned synergy. In experiments
involving 780,000 people in Wikipedia, 2.3 million people in Freebase, 500 million Web pages, and over 20 professional editors, TMI shows considerable annotation accuracy improvement (e.g., 70%) compared to baselines (e.g., 46%), especially for ``tail'' and emerging entities.Other authors
Patents
Projects
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Yahoo Tells You
WHY? -- It's time the not so net savvy got a chance to look up information without having to deal with Mr.Page Rank or mine through wikipedia. And why not help the 6th grader get on with his gully cricket while we deal with answering his homework?
WHAT? -- We give an easy interface to ask questions and if we know the answer, we answer them. We are able to answer questions which have a good consensus on yahoo answers. In case the question can't be answered, we redirect the user to the…WHY? -- It's time the not so net savvy got a chance to look up information without having to deal with Mr.Page Rank or mine through wikipedia. And why not help the 6th grader get on with his gully cricket while we deal with answering his homework?
WHAT? -- We give an easy interface to ask questions and if we know the answer, we answer them. We are able to answer questions which have a good consensus on yahoo answers. In case the question can't be answered, we redirect the user to the yahoo answers home page.
*DISCLAIMER* We currently don't deal with date/numeric/boolean/descriptive answers and time sensitive questions.
HOW? -- Our algorithm determines the answer in a 4 stage process
1. Query yahoo answers with user query and rank the questions.
2. Annotate the answers of the top ten ranked questions with wikipediaminer and extract entities.
3. Rank entities using our custom algo involving google'sOther creatorsSee project -
Semantic and Query processing in Resource Description Framework
Studied techniques on how to answer a query in an uncertain knowledge graph.
Involved analysis on ranking multiple entities where every entity is associated with some uncertainty. -
Jointly Bootstrapping Corpus Annotations and Typing Entities
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Wikipedia entities and annotated pages offer high-quality labeled data for training and evaluation. Un fortunately, Wikipedia features only one-ninth the number of entities as Freebase, and these are a highly biased sample of well-connected, frequently mentioned “head” entities. To bring hope to “tail” entities, we broaden our goal to a second task: assigning Wikipedia types to entities in Freebase but not ikipedia. The two tasks are synergistic: knowing the types of unfamiliar entities helps…
Wikipedia entities and annotated pages offer high-quality labeled data for training and evaluation. Un fortunately, Wikipedia features only one-ninth the number of entities as Freebase, and these are a highly biased sample of well-connected, frequently mentioned “head” entities. To bring hope to “tail” entities, we broaden our goal to a second task: assigning Wikipedia types to entities in Freebase but not ikipedia. The two tasks are synergistic: knowing the types of unfamiliar entities helps disambiguate
mentions, and words in mention contexts help assign types to entities. We present TMI, a bipartite graphical model for joint type mention inference.
Other creators -
Entity Linking by Weighting Wikipedia Catagories
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The ability to identify the named entities has been established as an important task in several areas, including topic detection and tracking, machine translation, and informa tion retrieval.
Entity Disambiguation or Entity Linking is the task of identifying mentions of entities from a catalog and linking them to the correct entities in a knowledge base. In recent work on entity annotation, the categories or types to which a candidate entity belongs has been used frequently for…The ability to identify the named entities has been established as an important task in several areas, including topic detection and tracking, machine translation, and informa tion retrieval.
Entity Disambiguation or Entity Linking is the task of identifying mentions of entities from a catalog and linking them to the correct entities in a knowledge base. In recent work on entity annotation, the categories or types to which a candidate entity belongs has been used frequently for disambiguation, in particular, for defining a notion of similarity between entities.However, in catalogs prepared by non-experts, such as Wikipedia or YAGO, categories can be peculiar, or at least low in topical meaning.
In this project, we proposed, implemented and evaluated some ways to associate a notion
of coherence or usefulness of categories, based on a limited training set of entity pairs and
their similarities
Other creatorsSee project -
Faceted Query Suggestions
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Suggesting queries based upon the diversity of the data(images). We did an analysis of various online unsupervised- non parametric clustering algorithms to cluster the images based upon their meta-data into different categories.
Every cluster was labelled using the meta-data of the images that got classified into the cluster.Other creators
Honors & Awards
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Infrastructure Track Winner
Sequoia Capital
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Winner at Sequoia::Hack 2015
Sequoia Capital
Built a platform to collect events from an app, without pushing any code to the app itself. Even the events can be decided at run-time, on what to track and what data to log.
What data should be tracked can be annotated by anyone using the debug version of the app, once its done, the customer facing app starts sending that data back without even updating the app.
This data can be consumed by any data analytics tool like Google Analytics, Mixpannel or any other private instances -
March 2014 Linkedin Hack Day Bangalore Finalist
LinkedIn
Built a real time restaurant table booking engine. Got shortlisted in top 9 most appreciated hacks out of more than 30 hacks.
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March 2014 WalmartLabs HackDay Bangalore Winner
@WalmartLabs
Developed and ideated a new approach by using the approach of sentiment analysis, to extract information from product and customer reviews.
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December 2012 Yahoo Student Award
Yahoo Research Labs
Received a scholarship grant for research project at IIT Bombay.
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July 2012 Yahoo Hack Day IIT Bombay
Yahoo
Prototyped and showcased a small version of an entity search engine. Winner amongst more than 100 hacks.
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