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Local and Global Algorithms for Disambiguation to Wikipedia
TLDR
We analyze approaches that utilize the Wikipedia link graph to arrive at coherent sets of disambiguations for a given document, and compare them to more traditional (local) approaches. Expand
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Unsupervised named-entity extraction from the Web: An experimental study
TLDR
The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, scalable manner. Expand
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Web-scale information extraction in knowitall: (preliminary results)
TLDR
This paper introduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner. Expand
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Locating Complex Named Entities in Web Text
TLDR
This paper investigates a novel approach to the first step in Web NER: locating complex named entities in Web text. Expand
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Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
TLDR
We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings. Expand
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TabEL: Entity Linking in Web Tables
TLDR
We present TabEL, a new EL system that performs the Entity Linking task on phrases in cells of Web tables. Expand
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Definition Modeling: Learning to Define Word Embeddings in Natural Language
TLDR
Distributed representations of words have been shown to capture lexical semantics, as demonstrated by their effectiveness in word similarity and analogical relation tasks. Expand
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KnowItNow: Fast, Scalable Information Extraction from the Web
TLDR
Numerous NLP applications rely on search-engine queries, both to extract information from and to compute statistics over the Web corpus. Expand
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Understanding the relationship between searchers' queries and information goals
TLDR
We describe results from Web search log studies aimed at elucidating user behaviors associated with queries and destination URLs that appear with different frequencies. Expand
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Further Experiments in the Evolution of Minimally Cognitive Behavior: From Perceiving Affordances to Selective Attention
TLDR
We extend previous work on the evolution of continuous-time recurrent neural networks for minimally cognitive behavior to a significantly wider range of tasks, including the perception of b-scaled affordances, self/nonself discrimination, short-term memory and selective attention. Expand
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