45,455 research outputs found
Ripple-down rules based open information extraction for the web documents
The World Wide Web contains a massive amount of information in unstructured natural language and obtaining valuable information from informally written Web documents is a major research challenge. One research focus is Open Information Extraction (OIE) aimed at developing relation-independent information extraction. Open Information Extraction systems seek to extract all potential relations from the text rather than extracting few pre-defined relations.
Previous machine learning-based Open Information Extraction systems require large volumes of labelled training examples and have trouble handling NLP tools errors caused by Web s informality. These systems used self-supervised learning that generates a labelled training dataset automatically using NLP tools with some heuristic rules. As the number of NLP tool errors increase because of the Web s informality, the self-supervised learning-based labelling technique produces noisy label and critical extraction errors.
This thesis presents Ripple-Down Rules based Open Information Extraction (RDROIE) an approach to Open Information Extraction that uses Ripple-Down Rules (RDR) incremental learning technique. The key advantages of this approach are that it does not require labelled training dataset and can handle the freer writing style that occurs in Web documents and can correct errors introduced by NLP tools. The RDROIE system, with minimal low-cost rule addition, outperformed previous OIE systems on informal Web documents
Knowledge Base Population using Semantic Label Propagation
A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge
Bases for Natural Language Processin
Self-supervised automated wrapper generation for weblog data extraction
Data extraction from the web is notoriously hard. Of the types of resources available on the web, weblogs are becoming increasingly important due to the continued growth of the blogosphere, but remain poorly explored. Past approaches to data extraction from weblogs have often involved manual intervention and suffer from low scalability. This paper proposes a fully automated information extraction methodology based on the use of web feeds and processing of HTML. The approach includes a model for generating a wrapper that exploits web feeds for deriving a set of extraction rules automatically. Instead of performing a pairwise comparison between posts, the model matches the values of the web feeds against their corresponding HTML elements retrieved from multiple weblog posts. It adopts a probabilistic approach for deriving a set of rules and automating the process of wrapper generation. An evaluation of the model is conducted on a dataset of 2,393 posts and the results (92% accuracy) show that the proposed technique enables robust extraction of weblog properties and can be applied across the blogosphere for applications such as improved information retrieval and more robust web preservation initiatives
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