A large amount of economic and financial news is now accessible through various news websites and social media platforms. Categorizing them into appropriate categories can be advantageous for various tasks, such as sentiment analysis and news-based market prediction. Unfortunately, news headlines categories may contain ambiguities due to the subjective nature of label assignment by authors or publishers. Consequently, achieving precise classification of news can be time-consuming and still reliant on human expertise. To tackle this challenging task, we proposed a hybrid approach to enhance the performance of economic and financial news classification. This approach combines baseline classifiers with a novel method called the Category Associated Feature Set (CAFS) classifier. CAFS transforms text input from the lexicon-space into the entity-space and discovers associations between entities and classes, akin to association rule learning. Experimental results on three datasets demonstrated that the proposed method is comparable to existing approaches and exhibits a significant improvement in the classification results for out-of-domain datasets. Additionally, employing CAFS in tandem with the existing text classification baselines can provide a general categorizer for distinguishing news categories across various sources without the need for extensive fine-tuning of the parameters associated with those classification baselines. This confirms that utilizing CAFS in a hybrid approach is appropriate and suitable for economic and financial news classification.

Person name disambiguation is essential to distinguish between persons that share the same name where unique identifiers are not present. In many domains this is a common problem including digital libraries where the same name can refer to multiple unique authors. Correctly attributing work and citations requires the digital library's database to be disambiguated. In this work we describe a large scale framework for disambiguating author names efficiently and effectively. The framework uses a density based clustering algorithm with a random forest based distance function to clusters unique authors. Effective use of blocking functions allows the clustering algorithm to be run in parallel. In our experiments we show that the framework disambiguates authors of more than 4 million papers in 24 hours.

Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents.They are often used as queries for retrieving records and also as key information for linking documents from multiple sources. Matching personal names can be challenging due to variations in spelling and various formatting of names. While many approximated name matching techniques have been proposed, most are generic string-matching algorithms. Unlike other types of proper names, personal names are highly cultural. Many ethnicities have their own unique naming systems and identifiable characteristics. In this paper we explore such relationships between ethnicities and personal names to improve the name matching performance. First, we propose a name-ethnicity classifier based on the multinomial logistic regression. Our model can effectively identify name-ethnicity from personal names in Wikipedia, which we use to define name-ethnicity, to within 85\% accuracy.Next, we propose a novel alignment-based name matching algorithm, based on Smith–Waterman algorithm and logistic regression.Different name matching models are then trained for different name-ethnicity groups.Our preliminary experimental result on DBLP's disambiguated author dataset yields a performance of 99\% precision and 89\% recall.Surprisingly, textual features carry more weight than phonetic ones in name-ethnicity classification.

Given a set of automatically extracted entities E of size n, we would like to cluster all the various names referring to the same canonical entity together. The variations of each entity include acronyms, full name, and informal naming conventions. We propose using search engine results to cluster variations of each entity based on the URLs appearing in those results. We create a cluster C for each top search result returned by querying for the entity e ∈ E assigning e to the cluster C. Our experiments on a manually created dataset shows that our approach achieves higher precision and recall than string matching algorithm and hierarchical clustering based disambiguation methods.

Document hierarchies provide views of a collection at different levels of granularity, making it easy to visualize and explore large document collections. Topic descriptors at each level of the hierarchy play an important role in helping users to achieve those