Text Item Classification Task
(Redirected from Text Classification Task)
- AKA: Text Categorization.
- input: a Text Item Set.
- output: a Text Category Set.
- Task Performance Measures: (see: Classification Task Performance Measure).
- It can be solved by a Text Categorization System that implements a Text Categorization Algorithm.
- It can range from being a Heuristic Text Classification Task to being a Data-Driven Text Classification Task.
- It can range from being a Manual Text Item Classification Task to being an Automated Text Item Classification Task.
- It can range, based on the text category set cardinality, from being
- a Terminological Term Classification Task.
- Webpage Type Classification: Classify a Webpage into categories such as a Product Review, "News Article", "Spam Webpage", or "Other" (a Multi-Valued Classification Task).
- Spam Email Classification: Classify an Email Message as to whether it a Spam Email Message or not, a Binary Classification Task.
- A Product Review Classification Task.
- A Text Sentiment Classification Task.
- A Newswire Topic Classification Task.
- a Sentence Classification Task.
- See: NLP Task, Semi-Supervised Text Processing, Text Visualization.
- (Mladeni; Brank; & Grobelnik, 2011) ⇒ Dunja Mladeni; Janez Brank; Marko Grobelnik. (2011). "Document Classification." In: (Sammut & Webb, 2011) p.289
- (Thet & al, 2007) ⇒ Tun Thura Thet, Jin-Cheon Na, and Christopher S. G. Khoo. (2007). "Filtering Product Reviews from Web Search Results." In: Proceedings of the 2007 ACM symposium on Document Engineering.
- (Ruch, 2006) ⇒ Patrick Ruch. (2006). "Automatic Assignment of Biomedical Categories: toward a generic approach." In: Bioinformatics, 2006 Mar 15. doi:10.1093/bioinformatics/bti783.
- QUOTE: To our knowledge the largest set of categories ever used by text classification systems has an order of magnitude of 104. Thus, Yang and Chute (1992) work with the International Classification of Diseases (about 12,000 concepts), while Yang (1999) and Wilbur and Yang (1996) report on experiments conducted with a search space of less than 18,000 Medical Subject Headings (MeSH). To evaluate our system, it is tested using two different benchmarks: 1) the OHSUGEN (Hersh, 2005) collection for the MeSH terminology and 2) the BioCreative data for the Gene Ontology (GO).
- (Sebastiani, 2002) ⇒ Fabrizio Sebastiani. (2002). "Machine Learning in Automated Text Categorization." In: Association of Computing Machinery Computing Surveys (CSUR), 34(1).
- The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories.
- (Yang, 1999) ⇒ Y. Yang. (1999). "An Evaluation of Statistical Approaches to Text Categorization." In: Journal of Information Retrieval, 1.
- NOTE: it experiments on a search space of ~18,000 Medical Subject Headings (MeSH).
- (McCallum, 1999) ⇒ Andrew McCallum. (1999). "Multi-label Text Classification with a Mixture Model Trained by EM." In: AAAI 99 Workshop on Text Learning.
- QUOTE: In many important document classification tasks, documents may each be associated with multiple class labels. ... Text classification is the problem of assigning a text document into one or more topic categories or classes. In multiclass document classification, as distinguished from binary document classification, there are more than two classes. In multi-label classification each document may have more than one class label. For example, given classes N. America, S. America, Europe, Asia and Australia, a news article about U.S. troops in Bosnia may be labeled with both the N. America and Europe classes.
- (Dumais & al, 1998) ⇒ Susan T. Dumais, John C. Platt, David Heckerman, and Mehran Sahami. (1998). "Inductive Learning Algorithms and Representations for Text Categorization." In: Proceedings of the Seventh International Conference on Information and Knowledge Management (CIKM 1998).
- Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. Its most widespread application to date has been for assigning subject categories to documents to support text retrieval, routing and filtering.
- (Wilbur & Yang, 1996) ⇒ J. Wilbur, and Y. Yang. (1996). "Analysis of Statistical Term Strength and its Use in the Indexing and Retrieval of Molecular Biology Texts." In: Comput. Biol. Med., 26(3), 209–222.
- experiment on a search space of less than 18,000 Medical Subject Headings (MeSH).
- (Yang & Chute, 1992) ⇒ Y. Yang, and C. Chute. (1992). "A Linear Least Squares Fit Mapping Method for Information Retrieval from Natural Language Texts." In: COLING 1992.
- Work with the International Classification of Diseases (about 12,000 concepts)
- (Field, 1975) ⇒ B. J. Field. (1975). "Towards Automatic Indexing: Automatic assignment of controlled-language indexing and classification from free indexing." In: : Journal of Documentation, 31(4). doi:10.1108/eb026605
- (Borko & Bernick, 1963) ⇒ Harold Borko, and Myrna Bernick. (1963). "Automatic Document Classification." In: Journal of the ACM (JACM).
- The problem of automatic document classification is a part of the larger problem of automatic content analysis. Classification means the determination of subject content. For a document to be classified under a given heading, it must be ascertained that its subject matter relates to that area of discourse. In most cases this is a relatively easy decision for a human being to make. The question being raised is whether a computer can be programmed to determine the subject content of a document and the category (categories) into which it should be classified.