Allen Institute for AI

DIY Information Extraction

DIY Information Extraction

DIY Information Extraction

Data scientists have a set of tools to work with structured data in tables. But how does one extract meaning from textual data? While NLP provides some solutions, they all require expertise in either machine learning, linguistics, or both. How do we expose advanced AI and text mining capabilities to domain experts who do not know ML or CS?
About DIY Information Extraction
  • Extractive search over CORD-19 with 3 powerful query modes | AI2 Israel, DIY Information Extraction

    SPIKE-CORD is powerful sentence-level, context-aware, and linguistically informed extractive search system for exploring the CORD-19 corpus.

    Try the demo
    SPIKE-CORD Demo Image
  • SPIKE-CORD Demo Image
    Extractive search over CORD-19 with 3 powerful query modes | AI2 Israel, DIY Information Extraction

    SPIKE-CORD is powerful sentence-level, context-aware, and linguistically informed extractive search system for exploring the CORD-19 corpus.

    Try the demo
  • SPIKE demo image
    Powerful extractive search | AI2 Israel, DIY Information Extraction

    SPIKE is a powerful sentence-level, context-aware, and linguistically informed extractive search system. Try SPIKE over one of our provided datasets.

    Try the demo
  • SPIKE demo image
    Powerful extractive search | AI2 Israel, DIY Information Extraction

    SPIKE is a powerful sentence-level, context-aware, and linguistically informed extractive search system. Try SPIKE over one of our provided datasets.

    Try the demo
    • Interactive Extractive Search over Biomedical Corpora

      Hillel Taub-Tabib, Micah Shlain, Shoval Sadde, Dan Lahav, Matan Eyal, Yaara Cohen, Yoav GoldbergACL2020We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries. In contrast to previous attempts to… more
    • pyBART: Evidence-based Syntactic Transformations for IE

      Aryeh Tiktinsky, Yoav Goldberg, Reut TsarfatyACL2020Syntactic dependencies can be predicted with high accuracy, and are useful for both machine-learned and pattern-based information extraction tasks. However, their utility can be improved. These syntactic dependencies are designed to accurately reflect syntactic relations, and they do not make… more
    • Syntactic Search by Example

      Micah Shlain, Hillel Taub-Tabib, Shoval Sadde, Yoav GoldbergACL2020We present a system that allows a user to search a large linguistically annotated corpus using syntactic patterns over dependency graphs. In contrast to previous attempts to this effect, we introduce a light-weight query language that does not require the user to know the details of the underlying… more

    Team

    AI2 Israel Members

    • Yoav Goldberg's Profile PhotoYoav GoldbergResearch Director, AI2 Israel
    • Hillel  Taub-Tabib's Profile PhotoHillel Taub-TabibResearch & Engineering
    • Micah Shlain's Profile PhotoMicah ShlainResearch & Engineering
    • Matan Eyal's Profile PhotoMatan EyalResearch & Engineering
    • Yaara Cohen's Profile PhotoYaara CohenEngineering
    • Shoval Sadde's Profile PhotoShoval SaddeLinguistics

    Interns

    • Aryeh Tiktinsky's Profile PhotoAryeh TiktinskyIntern
    • Shauli Ravfogel's Profile PhotoShauli RavfogelIntern