SVM Based Learning System for Information Extraction

Abstract

Abstract. This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-theart systems (including rule learning and statistical learning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the jobs corpus, and is second best on the remaining nine.

An Approach to Automatic Music Band Member Detection Based on Supervised Learning

“…Using the learned model, relevant information could be extracted also from new documents. Several approaches, more precisely, several types of machine learning algorithms, have been proposed for information extraction tasks, such as hidden-markov-models [3], decision trees [19], or support vector machines (SVM) [12]. The GATE framework offers a machine learning PR that supports various types of classification algorithms [13].…”

“…The GATE framework offers a machine learning PR that supports various types of classification algorithms [13]. Since examples in the literature (e.g., [12]) show that SVMs may yield results that rival those of rule-based approaches, SVMs are chosen as classifier.…”

“…Feature Construction Construction of the features for SVM training is carried out as described by Li et al [12]. Following their approach two distinct SVM classifiers are trained to detect Person entities to be marked as band members.…”

Using the Dempster-Shafer Theory of Evidence to Resolve ABox Inconsistencies

“…If we interpret the confidence value c attached to a statement returned by an extraction algorithm as a Bayesian probability value p, we, at the same time, introduce a belief that the statement is false with a probability 1 − p. However, the confidence of an extraction algorithm reflects only the belief that the document supports the statement and does not itself reflect the probability of a statement being false in the real world. Also while statistical extraction algorithms ( [13]) are able to assign a degree of probability to each extracted statement, rule-based algorithms ( [14,15]) can only assign the same confidence value to all statements extracted by the same rule based on the rule's performance on some evaluation set. Any extraction produced by a rule with a low confidence value in this case will serve as a negative evidence rather than simply lack of evidence.…”

Ontology-Based Information Extraction for Business Intelligence

“…Our future work will further improve our extraction tools incorporating machine learning capabilities into the extraction system [14], this will ensure that scalability is properly addressed in the extraction process. Our work on merging or ontology population will be extended to cover other semantic categories including locations, organisations, and specific business events (e.g., joint ventures).…”