Word Embedding and String-Matching Techniques for Automobile Entity Name Identification from Web Reviews

Abstract

With the huge popularity of Internet, various types of information on a wide range of domains are floating over different social media platforms. To extract this information for using in diverse natural language processing applications, identifying the names is prerequisite. A study is presented here, to identify automobile names from noisy web reviews by exploring two widely used machine learning algorithms, Conditional Random Field and Support Vector Machine. The accuracy of machine learning classifiers radically rely on size and quality of training data which has been prepared manually by extracting discussion forum corpus; the task is time consuming and laborious; hence to leverage this word embedding is adopted. Though it enhances the system's performance but is unable to spot noisy names which occur in web reviews. Next, a gazetteer based string matching technique is proposed, it recognizes a new set of noisy automobile entities, resulting considerable improvement in accuracy.

Proposed threshold-based and rule-based approaches to detecting duplicates in bibliographic database

“…Once a model has been developed, it can be measured using a confusion matrix that compares actual and predicted classification results [40]. The confusion matrix comprises of true positive (TP), false positive (FP), false negative (FN), and true negative (TN) as classification evaluation parameters [41].…”