4 research outputs found
Semi-Supervised Event Extraction with Paraphrase Clusters
Supervised event extraction systems are limited in their accuracy due to the
lack of available training data. We present a method for self-training event
extraction systems by bootstrapping additional training data. This is done by
taking advantage of the occurrence of multiple mentions of the same event
instances across newswire articles from multiple sources. If our system can
make a highconfidence extraction of some mentions in such a cluster, it can
then acquire diverse training examples by adding the other mentions as well.
Our experiments show significant performance improvements on multiple event
extractors over ACE 2005 and TAC-KBP 2015 datasets.Comment: NAACL 201
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Explainable improved ensembling for natural language and vision
Ensemble methods are well-known in machine learning for improving prediction
accuracy. However, they do not adequately discriminate among underlying
component models. The measure of how good a model is can sometimes be estimated
from “why” it made a specific prediction. We propose a novel approach
called Stacking With Auxiliary Features (SWAF) that effectively leverages component
models by integrating such relevant information from context to improve
ensembling. Using auxiliary features, our algorithm learns to rely on systems that
not just agree on an output prediction but also the source or origin of that output.
We demonstrate our approach to challenging structured prediction problems
in Natural Language Processing and Vision including Information Extraction, Object
Detection, and Visual Question Answering. We also present a variant of SWAF
for combining systems that do not have training data in an unsupervised ensemble
with systems that do have training data. Our combined approach obtains a new
state-of-the-art, beating our prior performance on Information Extraction.
The state-of-the-art systems on many AI applications are ensembles of deeplearning
models. These models are hard to interpret and can sometimes make odd
mistakes. Explanations make AI systems more transparent and also justify their
predictions. We propose a scalable approach to generate visual explanations for
ensemble methods using the localization maps of the component systems. Crowdsourced
human evaluation on two new metrics indicates that our ensemble’s explanation
significantly qualitatively outperforms individual systems’ explanations.Computer Science
Concept and entity grounding using indirect supervision
Extracting and disambiguating entities and concepts is a crucial step toward understanding natural language text. In this thesis, we consider the problem of grounding concepts and entities mentioned in text to one or more knowledge bases (KBs). A well-studied scenario of this problem is the one in which documents are given in English and the goal is to identify concept and entity mentions, and find the corresponding entries the mentions refer to in Wikipedia. We extend this problem in two directions: First, we study identifying and grounding entities written in any language to the English Wikipedia. Second, we investigate using multiple KBs which do not contain rich textual and structural information Wikipedia does.
These more involved settings pose a few additional challenges beyond those addressed in the standard English Wikification problem. Key among them is that no supervision is available to facilitate training machine learning models. The first extension, cross-lingual Wikification, introduces problems such as recognizing multilingual named entities mentioned in text, translating non-English names into English, and computing word similarity across languages. Since it is impossible to acquire manually annotated examples for all languages, building models for all languages in Wikipedia requires exploring indirect or incidental supervision signals which already exist in Wikipedia. For the second setting, we need to deal with the fact that most KBs do not contain the rich information Wikipedia has; consequently, the main supervision signal used to train Wikification rankers does not exist anymore. In this thesis, we show that supervision signals can be obtained by carefully examining the redundancy and relations between multiple KBs. By developing algorithms and models which harvest these incidental signals, we can achieve better performance on these tasks