@inproceedings{haq-etal-2023-angel,
    title = "Angel: Enterprise Search System for the Non-Profit Industry",
    author = "Haq, Saiful  and
      Sharma, Ashutosh  and
      Bhattacharyya, Pushpak",
    editor = "Wang, Mingxuan  and
      Zitouni, Imed",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.emnlp-industry.77/",
    doi = "10.18653/v1/2023.emnlp-industry.77",
    pages = "828--835",
    abstract = "Non-profit industry need a system for accurately matching fund-seekers (e.g., AMERICAN NATIONAL RED CROSS) with fund-givers (e.g., BILL AND MELINDA GATES FOUNDATION) aligned in cause (e.g., cancer) and target beneficiary group (e.g., children). In this paper, we create an enterprise search system {\textquotedblleft}ANGEL{\textquotedblright} for the non-profit industry that takes a fund-giver`s mission description as input and returns a ranked list of fund-seekers as output, and vice-versa. ANGEL employs ColBERT, a neural information retrieval model, which we enhance by exploiting the two techniques of (a) Syntax-aware local attention (SLA) to combine syntactic information in the mission description with multi-head self-attention and (b) Dense Pseudo Relevance Feedback (DPRF) for augmentation of short mission descriptions. We create a mapping dictionary {\textquotedblleft}non-profit-dict{\textquotedblright} to curate a {\textquotedblleft}non-profit-search database{\textquotedblright} containing information on 594K fund-givers and 194K fund-seekers from IRS-990 filings for the non-profit industry search engines . We also curate a {\textquotedblleft}non-profit-evaluation{\textquotedblright} dataset containing scored matching between 463 fund-givers and 100 fund-seekers. The research is in collaboration with a philanthropic startup that identifies itself as an {\textquotedblleft}AI matching platform, fundraising assistant, and philanthropy search base.{\textquotedblright} Domain experts at the philanthropic startup annotate the non-profit evaluation dataset and continuously evaluate the performance of ANGEL. ANGEL achieves an improvement of 0.14 MAP@10 and 0.16 MRR@10 over the state-of-the-art baseline on the non-profit evaluation dataset. To the best of our knowledge, ours is the first effort at building an enterprise search engine based on neural information retrieval for the non-profit industry."
}