NAMED ENTITIY RECOGNITION and ATTRIBUTE EXTRACTION using MACHINE LEARNING ALGORITHMS

Information Technology Engineering

Named-entity recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities in text into pre-defined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. In focused NER, once the entities are recognised we further aim at finding the most important named entities among all the others in a document, which we refer to as focused named entity recognition. We implement this using a classifier approach, i.e. Naïve Bayes classification, and we show that these focused named entities are useful for many natural language processing applications, such as document summarization, search result ranking, and entity detection and tracking. Attribute extraction on the other hand, involves automatic selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive problem you are working on. Many researchers had proposed rule based or statistic based approaches to deal with the extraction task in a variety of application areas. Here we try to implement an approach to extract the entities’ attributes from unstructured text corpus Our goal can be twofold in this respect, firstly we can aim at simply organizing information so that it is useful to people, or put it in a semantically precise form to make further inferences using algorithms. In the current market scenario, big data is at the crowning point of the latest technology. Big data involves not just collection but manipulation in a way that we can develop prescriptive and predictive models from it, and extract patterns that prove of value to the customers. Machine learning involves the task of providing the computer with a decision making capability, basically a computer mimics the human mind and develops intelligent behavior. Within machine learning algorithms, we have the task of natural language processing. NLP has the subtask of Information extraction, and many other applications like named entity recognition, speech recognition, optical character recognition, word sense disambiguation, etc. Reading about this further motivated us to research in this field, and implement systems that can be used for these applications. We chose this project with an interest to learn about the applications of machine learning algorithms, and implement a system for entity recognition and attribute extraction.

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