Siddhanth Jain

Bengaluru, Karnataka, India
3K followers 500+ connections

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Publications

  • 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.

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Patents

  • Analyzing customer reviews to determine feature score for products

    Filed US 14846459

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Projects

  • 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's

    Other creators
    See 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

    -

    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

    -

    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 creators
    See project
  • Faceted Query Suggestions

    -

    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.

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Honors & Awards

  • Infrastructure Track Winner

    Sequoia Capital

  • 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.

  • 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.

  • December 2012 Yahoo Student Award

    Yahoo Research Labs

    Received a scholarship grant for research project at IIT Bombay.

  • 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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