Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Eunsol Choi
- sl:arxiv_num : 1807.04905
- sl:arxiv_published : 2018-07-13T04:19:03Z
- sl:arxiv_summary : We introduce a new entity typing task: given a sentence with an entity
mention, the goal is to predict a set of free-form phrases (e.g. skyscraper,
songwriter, or criminal) that describe appropriate types for the target entity.
This formulation allows us to use a new type of distant supervision at large
scale: head words, which indicate the type of the noun phrases they appear in.
We show that these ultra-fine types can be crowd-sourced, and introduce new
evaluation sets that are much more diverse and fine-grained than existing
benchmarks. We present a model that can predict open types, and is trained
using a multitask objective that pools our new head-word supervision with prior
supervision from entity linking. Experimental results demonstrate that our
model is effective in predicting entity types at varying granularity; it
achieves state of the art performance on an existing fine-grained entity typing
benchmark, and sets baselines for our newly-introduced datasets. Our data and
model can be downloaded from: http://nlp.cs.washington.edu/entity_type@en
- sl:arxiv_title : Ultra-Fine Entity Typing@en
- sl:arxiv_updated : 2018-07-13T04:19:03Z
- sl:bookmarkOf : https://arxiv.org/abs/1807.04905
- sl:creationDate : 2021-06-22
- sl:creationTime : 2021-06-22T10:50:58Z
Documents with similar tags (experimental)
Préférences...