Hättasch, Benjamin ; Bodensohn, Jan-Micha ; Binnig, Carsten (2022)
Demonstrating ASET: Ad-Hoc Structured Exploration of Text Collections.
2022 International Conference on Management of Data. Philadelphia, USA (12.-17.06.2022)
doi: 10.1145/3514221.3520174
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
In this demo, we present ASET, a novel tool to explore the contents of unstructured data (text) by automatically transforming relevant parts into tabular form. ASET works in an ad-hoc manner without the need to curate extraction pipelines for the (unseen) text collection or to annotate large amounts of training data. The main idea is to use a new two-phased approach that first extracts a superset of information nuggets from the texts using existing extractors such as named entity recognizers. In a second step, it leverages embeddings and a novel matching strategy to match the extractions to a structured table definition as requested by the user. This demo features the ASET system with a graphical user interface that allows people without machine learning or programming expertise to explore text collections efficiently. This can be done in a self-directed and flexible manner, and ASET provides an intuitive impression of the result quality.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Hättasch, Benjamin ; Bodensohn, Jan-Micha ; Binnig, Carsten |
| Art des Eintrags: | Bibliographie |
| Titel: | Demonstrating ASET: Ad-Hoc Structured Exploration of Text Collections |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2022 |
| Verlag: | ACM |
| Buchtitel: | SIGMOD'22: Proceedings of the 2022 International Conference on Management of Data |
| Veranstaltungstitel: | 2022 International Conference on Management of Data |
| Veranstaltungsort: | Philadelphia, USA |
| Veranstaltungsdatum: | 12.-17.06.2022 |
| DOI: | 10.1145/3514221.3520174 |
| Kurzbeschreibung (Abstract): | In this demo, we present ASET, a novel tool to explore the contents of unstructured data (text) by automatically transforming relevant parts into tabular form. ASET works in an ad-hoc manner without the need to curate extraction pipelines for the (unseen) text collection or to annotate large amounts of training data. The main idea is to use a new two-phased approach that first extracts a superset of information nuggets from the texts using existing extractors such as named entity recognizers. In a second step, it leverages embeddings and a novel matching strategy to match the extractions to a structured table definition as requested by the user. This demo features the ASET system with a graphical user interface that allows people without machine learning or programming expertise to explore text collections efficiently. This can be done in a self-directed and flexible manner, and ASET provides an intuitive impression of the result quality. |
| Freie Schlagworte: | systems_aset, systems_wannadb, systems_intexplore, text to table, interactive text exploration, matching embeddings |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Data and AI Systems |
| Hinterlegungsdatum: | 06 Jun 2023 12:37 |
| Letzte Änderung: | 02 Aug 2023 13:36 |
| PPN: | 510088619 |
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