منابع مشابه
Distant Supervision for Relation Extraction with Ranking-Based Methods
Relation extraction has benefited from distant supervision in recent years with the development of natural language processing techniques and data explosion. However, distant supervision is still greatly limited by the quality of training data, due to its natural motivation for greatly reducing the heavy cost of data annotation. In this paper, we construct an architecture called MIML-sort (Mult...
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Supervised machine learning methods have been widely used in relation extraction that finds the relation between two named entities in a sentence. However, their disadvantages are that constructing training data is a cost and time consuming job, and the machine learning system is dependent on the domain of the training data. To overcome these disadvantages, we construct a weakly labeled data se...
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Distant supervision, a paradigm of relation extraction where training data is created by aligning facts in a database with a large unannotated corpus, is an attractive approach for training relation extractors. Various models are proposed in recent literature to align the facts in the database to their mentions in the corpus. In this paper, we discuss and critically analyse a popular alignment ...
متن کاملDistant Supervision for Relation Extraction with Matrix Completion
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features. To tackle the sparsity and noise challenges, we propose solving the classification problem using matrix completion on factorized matrix of minimized rank. We formulate relation classification as completing the unknown labels of testing items (ent...
متن کاملDistant Supervision for Relation Extraction with an Incomplete Knowledge Base
Distant supervision, heuristically labeling a corpus using a knowledge base, has emerged as a popular choice for training relation extractors. In this paper, we show that a significant number of “negative“ examples generated by the labeling process are false negatives because the knowledge base is incomplete. Therefore the heuristic for generating negative examples has a serious flaw. Building ...
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ژورنال
عنوان ژورنال: Entropy
سال: 2016
ISSN: 1099-4300
DOI: 10.3390/e18060204