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Distant supervision for relation extraction without labeled data
This work investigates an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACE-style algorithms, and allowing the use of corpora of any size.
Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks
This work explores the use of Amazon's Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web, and proposes a technique for bias correction that significantly improves annotation quality on two tasks.
Learning Syntactic Patterns for Automatic Hypernym Discovery
This paper presents a new algorithm for automatically learning hypernym (is-a) relations from text, using "dependency path" features extracted from parse trees and introduces a general-purpose formalization and generalization of these patterns.
Semantic Taxonomy Induction from Heterogenous Evidence
This work proposes a novel algorithm for inducing semantic taxonomies that flexibly incorporates evidence from multiple classifiers over heterogenous relationships to optimize the entire structure of the taxonomy, using knowledge of a word's coordinate terms to help in determining its hypernyms, and vice versa.
Learning to Merge Word Senses
A discriminative classifier is trained over a wide variety of features derived from WordNet structure, corpus-based evidence, and evidence from other lexical resources, and a learned similarity measure outperforms previously proposed automatic methods for sense clustering on the task of predicting human sense merging judgments.
Smoothing techniques for adaptive online language models: topic tracking in tweet streams
Experiments show that unigram language models smoothed using a normalized extension of stupid backoff and a simple queue for history retention performs well on the task of tracking broad topics in continuous streams of short texts from the microblogging service Twitter.
Learning Named Entity Hyponyms for Question Answering
It is demonstrated how a recently developed statistical approach to mining such relations can be tailored to identify named entity hyponyms, and how as a result, superior question answering performance can be obtained.
Effectively Using Syntax for Recognizing False Entailment
A novel framework for recognizing textual entailment that focuses on the use of syntactic heuristics to recognize false entailment is presented, which demonstrates state-of-the-art performance on a widely-used test set.
Syntactic Contributions in the Entailment Task: an implementation
The data set made available by the PASCAL Rec-ognizing Textual Entailment Challenge provides a great opportunity to focus on the very difficult task of determining whether one sentence is entailed by another, and an accuracy of 74% is in principle achievable for a system with access to a general purpose thesaurus.