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Showing 1 to 4 of 4 for “"entity identification"”.

  1. Toward Concept-Based Text Understanding and Mining

    In addition to entity identification, we also extend the generative probabilistic model to address a significant application that are related to concept-based text understanding and mining---semantic integration between text and databases, based on entity identification and tracking.

    uiuc Repository record for Toward Concept-Based Text Understanding and Mining (opens in a new tab)

  2. Deep Learning for Unstructured Data by Leveraging Domain Knowledge

    … such as machine translation, POS tagging, named entity recognition, etc. However, when it comes to informal and noisy text data, such as tweets, HTMLs, OCR, there are two major issues with modern deep learning technologies. First, deep learning requires large amount of labeled data to train an …

    temple Repository record for Deep Learning for Unstructured Data by Leveraging Domain Knowledge (opens in a new tab)

  3. Entity-centric search: querying by entities and for entities

    … on the Web nowadays are about entities, i.e., entity-centric, for example, finding cameras that have black color and high resolution, keeping track of important events of a favorite celebrity, etc.. However, without modeling the concept of entities, most search engines still take the page view …

    uiuc Repository record for Entity-centric search: querying by entities and for entities (opens in a new tab)

  4. Domain-agnostic named entity recognition on unstructured text

    Named Entity Recognition (NER) is the task of extracting informing entities belonging to predefined semantic classes from raw text. These semantic classes could be general-purpose like a person, location or domain-specific like genes, protein names in biomedical texts. NER has widespread …

    uiuc Repository record for Domain-agnostic named entity recognition on unstructured text (opens in a new tab)