Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ultra-Fine Entity Typing   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

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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin