Tasks and Subtasks
Applying information extraction on text, is linked to the problem of text simplification in order to create a structured view of the information present in free text. The overall goal being to create a more easily machine-readable text to process the sentences. Typical subtasks of IE include:
- Named entity extraction which could include:
- Named entity recognition: recognition of known entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions, employing existing knowledge of the domain or information extracted from other sentences. Typically the recognition task involves assigning a unique identifier to the extracted entity. A simpler task is named entity detection, which aims to detect entities without having any existing knowledge about the entity instances. For example, in processing the sentence "M. Smith likes fishing", named entity detection would denote detecting that the phrase "M. Smith" does refer to a person, but without necessarily having (or using) any knowledge about a certain M. Smith who is (/or, "might be") the specific person whom that sentence is talking about.
- Coreference resolution: detection of coreference and anaphoric links between text entities. In IE tasks, this is typically restricted to finding links between previously-extracted named entities. For example, "International Business Machines" and "IBM" refer to the same real-world entity. If we take the two sentences "M. Smith likes fishing. But he doesn't like biking", it would be beneficial to detect that "he" is referring to the previously detected person "M. Smith".
- Relationship extraction: identification of relations between entities, such as:
- PERSON works for ORGANIZATION (extracted from the sentence "Bill works for IBM.")
- PERSON located in LOCATION (extracted from the sentence "Bill is in France.")
- Semi-structured information extraction which may refer to any IE that tries to restore some kind information structure that has been lost through publication such as:
- Table extraction: finding and extracting tables from documents.
- Comments extraction : extracting comments from actual content of article in order to restore the link between author of each sentence
- Language and vocabulary analysis
- Terminology extraction: finding the relevant terms for a given corpus
- Audio extraction
- Template-based music extraction: finding relevant characteristic in an audio signal taken from a given repertoire; for instance time indexes of occurrences of percussive sounds can be extracted in order to represent the essential rhythmic component of a music piece.
Note this list is not exhaustive and that the exact meaning of IE activities is not commonly accepted and that many approaches combine multiple sub-tasks of IE in order to achieve a wider goal. Machine learning, statistical analysis and/or natural language processing are often used in IE.
IE on non-text documents is becoming an increasing topic in research and information extracted from multimedia documents can now be expressed in a high level structure as it is done on text. This naturally lead to the fusion of extracted information from multiple kind of documents and sources.
Read more about this topic: Information Extraction
Famous quotes containing the word tasks:
“We are all adult learners. Most of us have learned a good deal more out of school than in it. We have learned from our families, our work, our friends. We have learned from problems resolved and tasks achieved but also from mistakes confronted and illusions unmasked. . . . Some of what we have learned is trivial: some has changed our lives forever.”
—Laurent A. Daloz (20th century)