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Open Information Extraction from the Web
TLDR
This paper introduces Open IE (OIE), a new extraction paradigm where the system makes a single data-driven pass over its corpus and extracts a large set of relational tuples without requiring any human input. Expand
The Tradeoffs Between Open and Traditional Relation Extraction
TLDR
We present a new model for Open IE called O-CRF and show that it achieves increased precision and nearly double the recall than TEXTRUNNER, the previous stateof-the-art Open IE system. Expand
Scaling to Very Very Large Corpora for Natural Language Disambiguation
TLDR
The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Expand
Web question answering: is more always better?
TLDR
This paper describes a question answering system that is designed to capitalize on the tremendous amount of data that is now available online. Expand
Headline Generation Based on Statistical Translation
TLDR
This paper presents an alternative to extractive summarization: an approachthat makes it possibleto generatecoherentsummariesthat are shorter than a single sentenceand that attempt to conform to a particularstyle. Expand
TextRunner: Open Information Extraction on the Web
TLDR
We demonstrate a new kind of information extraction, called Open Information Extraction (OIE), in which the system makes a single, data-driven pass over the entire corpus and extracts a large set of relational tuples, without requiring any human input. Expand
Data-Intensive Question Answering
TLDR
Utilisation de la redondance des reponses elles-memes pour ameliorer le resultat final de la recherche d'information-redondance due a la tres grande quantite d'informations disponibles actuellement Microsoft Research Redmond participated for the first time in TREC this year. Expand
An Analysis of the AskMSR Question-Answering System
TLDR
We describe the architecture of the AskMSR Question answering system and systematically evaluate contributions of different system components to accuracy. Expand
Part-of-Speech Tagging in Context
TLDR
We present a new HMM tagger that exploits context on both sides of a word to be tagged, and evaluate it in both the unsupervised and supervised case. Expand
Machine Reading
TLDR
We place the notion of “Machine Reading” in context, describe progress towards this goal by the KnowItAll research group at the University of Washington, and highlight several open questions. Expand
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