Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Features and Aggregators for Web-scale Entity Search   We focus on two research issues in entity search: scoring a document or
snippet that potentially supports a candidate entity, and aggregating scores
from different snippets into an entity score. Proximity scoring has been
studied in IR outside the scope of entity search. However, aggregation has been
hardwired except in a few cases where probabilistic language models are used.
We instead explore simple, robust, discriminative ranking algorithms, with
informative snippet features and broad families of aggregation functions. Our
first contribution is a study of proximity-cognizant snippet features. In
contrast with prior work which uses hardwired "proximity kernels" that
implement a fixed decay with distance, we present a "universal" feature
encoding which jointly expresses the perplexity (informativeness) of a query
term match and the proximity of the match to the entity mention. Our second
contribution is a study of aggregation functions. Rather than train the ranking
algorithm on snippets and then aggregate scores, we directly train on entities
such that the ranking algorithm takes into account the aggregation function
being used. Our third contribution is an extensive Web-scale evaluation of the
above algorithms on two data sets having quite different properties and
behavior. The first one is the W3C dataset used in TREC-scale enterprise
search, with pre-annotated entity mentions. The second is a Web-scale
open-domain entity search dataset consisting of 500 million Web pages, which
contain about 8 billion token spans annotated automatically with two million
entities from 200,000 entity types in Wikipedia. On the TREC dataset, the
performance of our system is comparable to the currently prevalent systems. On
the much larger and noisier Web dataset, our system delivers significantly
better performance than all other systems, with 8% MAP improvement over the
closest competitor.

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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: astro-ph
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The categories of this paper are: cmp-lg
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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: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph