Terminological Extraction is also referred to as chunking. Chunking is a process of extracting small pieces of information from a larger piece of text. This can be done for a variety of purposes, such as identifying key topics in a document, generating a taxonomy or ontology, or improving the accuracy of information retrieval systems.
Chunking is generally performed using some sort of algorithm or machine learning technique, rather than being done manually. The most appropriate method will depend on the nature of the text data and the desired outcome.
There are a number of different methods that can be used for chunking, including rule-based approaches, statistical methods and machine learning techniques. The most appropriate method will depend on the nature of the text data and the desired outcome.
Tools for Terminological Extraction
There are a number of different software tools available for terminological extraction, including:
- Apache UIMA
- GATE
- KNIME
- OpenCalais
- RapidMiner
- SAS Text Miner
- TAMS Analyzer
Comparison to Other Terms
Terminological Extraction is sometimes confused with other terms, such as Named Entity Recognition (NER) and Information Extraction (IE). However, there are some important differences between these terms:
- NER is primarily concerned with identifying proper names, such as people, places and organizations, whereas Terminological Extraction does not make this distinction.
- IE usually refers to a more general process of extracting structured information from unstructured text, while Terminological Extraction is more specific and focused on extracting terminology.
- Terminological Extraction can be used as a component of IE, but it is not the same thing.
In general, Terminological Extraction is a more specific and focused task than NER or IE, and it is concerned with extracting terminology rather than proper names or other types of information.
Benefits of Terminological Extraction
There are a number of benefits that can be gained from performing terminological extraction, including:
- Improved information retrieval – by identifying and extracting key terms from text documents, it is possible to improve the accuracy of information retrieval systems.
- Generation of ontologies and taxonomies – terminological extraction can be used to automatically generate ontologies and taxonomies from text data.
- Identification of key topics in text data – by extracting key terms from text data, it is possible to identify the main topics that are being discussed.