Minimally-supervised Methods for Arabic Named Entity Recognition

Named Entity Recognition (NER) has attracted much attention over the past twenty years, as a main task of Information Extraction. The current dominant techniques for addressing NER are supervised methods that can achieve high performance, but require new manually annotated data for every new domain and/or genre change. Our work focuses on approaches that make it possible to tackle new domains with minimal human intervention to identify Named Entities (NEs) in Arabic text. Specifically, we investigate two minimally-supervised methods: semi-supervised learning and distant learning. Our semi-supervised algorithm for identifying NEs does not require annotated training data or gazetteers. It only requires, for each NE type, a seed list of a few instances to initiate the learning process. Novel aspects of our algorithm include (i) a new way to produce and generalise the extraction patterns (ii) a new filtering criterion to remove noisy patterns (iii) a comparison of two ranking measures for determining the most reliable candidate NEs. Next, we present our methodology to exploit Wikipedia structure to automatically develop an Arabic NE annotated corpus. A novel mechanism is introduced, based on the high coverage of Wikipedia, in order to address two challenges particular to tagging NEs in Arabic text: rich morphology and the absence of capitalisation. Neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised algorithms tend to have high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. Therefore, we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We used a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure, recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best minimally-supervised classifier

Feature Extraction and Duplicate Detection for Text Mining: A Survey

Text mining, also known as Intelligent Text Analysis is an important research area. It is very difficult to focus on the most appropriate information due to the high dimensionality of data. Feature Extraction is one of the important techniques in data reduction to discover the most important features. Proce- ssing massive amount of data stored in a unstructured form is a challenging task. Several pre-processing methods and algo- rithms are needed to extract useful features from huge amount of data. The survey covers different text summarization, classi- fication, clustering methods to discover useful features and also discovering query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query thereby reducing time taken by the user. Dealing with collection of text documents, it is also very important to filter out duplicate data. Once duplicates are deleted, it is recommended to replace the removed duplicates. Hence we also review the literature on duplicate detection and data fusion (remove and replace duplicates).The survey provides existing text mining techniques to extract relevant features, detect duplicates and to replace the duplicate data to get fine grained knowledge to the user