Turkish Wikipedia Named-Entity Recognition and Text Categorization (TWNERTC) dataset is a collection of automatically categorized and annotated sentences obtained from Wikipedia. We constructed large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 77 different domains. Since automated processes are prone to ambiguity, we also introduce two new content specific noise reduction methodologies. Moreover, we map fine-grained entity types to the equivalent four coarse-grained types: person, loc, org, misc. Eventually, we construct six different dataset versions and evaluate the quality of annotations by comparing ground truths from human annotators. We make these datasets publicly available to support studies on Turkish named-entity recognition (NER) and text categorization (TC).
Constructing a comprehensive dataset for TC is tougher than NER since there is no limit for the number of categories that are represented in such sets. In general, there are many TC datasets available in English for many different problems such as sentiment analysis @cite_31 and categorizing gender @cite_1 . The largest and the most popular dataset among them is Reuters Corpus Volume 1 (RCV1) which consists of manually categorized 800K news stories with over 100 sub-categories under three different main categories @cite_17 . This version the dataset has problems with document categories and suffers from lack of documentation about the dataset. propose an improved version of this dataset with reduced categorization mistakes and provide a better documentation @cite_2 .
@cite_2 @cite_31 @cite_1 @cite_17
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Reuters Corpus Volume I (RCV1) is an archive of over 800,000 manually categorized newswire stories recently made available by Reuters, Ltd. for research purposes. Use of this data for research on text categorization requires a detailed understanding of the real world constraints under which the data was produced. Drawing on interviews with Reuters personnel and access to Reuters documentation, we describe the coding policy and quality control procedures used in producing the RCV1 data, the intended semantics of the hierarchical category taxonomies, and the corrections necessary to remove errorful data. We refer to the original data as RCV1-v1, and the corrected data as RCV1-v2. We benchmark several widely used supervised learning methods on RCV1-v2, illustrating the collection's properties, suggesting new directions for research, and providing baseline results for future studies. We make available detailed, per-category experimental results, as well as corrected versions of the category assignments and taxonomy structures, via online appendices. Aspect extraction aims to extract fine-grained opinion targets from opinion texts. Recent work has shown that the syntactical approach, which employs rules about grammar dependency relations between opinion words and aspects, performs quite well. This approach is highly desirable in practice because it is unsupervised and domain independent. However, the rules need to be carefully selected and tuned manually so as not to produce too many errors. Although it is easy to evaluate the accuracy of each rule automatically, it is not easy to select a set of rules that produces the best overall result due to the overlapping coverage of the rules. In this paper, we propose a novel method to select an effective set of rules. To our knowledge, this is the first work that selects rules automatically. Our experiment results show that the proposed method can select a subset of a given rule set to achieve significantly better results than the full rule set and the existing state-of-the-art CRF-based supervised method. Reuters, the global information, news and technology group, has for the first time made available free of charge, large quantities of archived Reuters news stories for use by research communities around the world. The Reuters Corpus Volume 1 (RCV1) includes over 800,000 news stories typical of the annual English language news output of Reuters. This paper describes the origins of RCV1, the motivations behind its creation, and how it differs from previous corpora. In addition we discuss the system of category coding, whereby each story is annotated for topic, region and industry sector. We also discuss the process by which these codes were applied, and examine the issues involved in maintaining quality and consistency of coding in an operational, commercial environment.