Information Extraction Task

An Information Extraction Task is a Data Analysis Task that requires the populating a Data Structure from the Data contained in a set of Artifacts.

• AKA: Information Extraction, IE, Template Filling, Semantic Frame Filling, Concept Extraction Task.
• Context:
  • Input:
    • an Artifact (typically a Human Readable Artifact).
    • Optional Input, a Data Structure.
  • Output:
    • a populated Data Structure (with Data Records).
    • Optional Output, an Information Extraction Pattern.
  • Metrics: Output Correctness (e.g. Accuracy, Redundancy Metric, ...)
  • Metrics: Computational Complexity, and Space Complexity).
  • It can be solved by a Information Extraction System that implements an Information Extraction Algorithm.
  • It can range from being:
    • an Open Information Extraction Task, were a Data Structure is not provided,
    • to a Closed Information Extraction Task, were a Data Structure is provided.
  • It can be supported by:
    • an Information Retrieval Task.
    • an Entity Mention Recognition Task,
    • an Entity Mention Coreference Resolution Task,
    • an Entity Mention Normalization Task,
    • a Semantic Relation Mention Recognition Task,
    • a Duplicate Record Detection Task, to identify records with redundant information.
    • a Record Canonicalization Task, to create a single non-redundant record.
  • It can be:
    • an Ontology Population Task, if the Data Structure is an Ontology
    • a Relational Table Population Task, if the Data Structure is a Relational Table
  • KindOf: It can be an Information Extraction from Text Task, Information Extraction from Documents Task, etc.
  • It can support a Question Answering Task because this task has slightly stronger requirements. E.g. questions are not limited to ones such as: "What are the headquarter locations for all companies?"
  • It can enable the extraction of actionable Knowledge from Unstructured Text or Semi-Structured Documents.
• Example(s):
  • a Citation Extraction Task, that can support a Citation Search Service.
  • A Biomedical Information Extraction Task
  • The extraction of Consumer Product Data to facilitate Searching (e.g. Google Products[1])
  • Employment Posting Extraction and Resume Extraction.
  • see ACE Project.
  • see MUC.
• See: Data Curation Task.

References

2009

• (Wikipedia, 2009) http://en.wikipedia.org/wiki/Information_extraction
  • In natural language processing, information extraction (IE) is a type of information retrieval whose goal is to automatically extract structured information, i.e. categorized and contextually and semantically well-defined data from a certain domain, from unstructured machine-readable documents. An example of information extraction is the extraction of instances of corporate mergers, more formally MergerBetween(company1,company2,date), from an online news sentence such as: "Yesterday, New-York based Foo Inc. announced their acquisition of Bar Corp." A broad goal of IE is to allow computation to be done on the previously unstructured data. A more specific goal is to allow logical reasoning to draw inferences based on the logical content of the input data.
  • The significance of IE is determined by the growing amount of information available in unstructured (i.e. without metadata) form, for instance on the Internet. This knowledge can be made more accessible by means of transformation into relational form, or by marking-up with XML tags. An intelligent agent monitoring a news data feed requires IE to transform unstructured data into something that can be reasoned with.
  • Typical subtasks of IE are:
    • Named Entity Recognition: recognition of entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions.
    • Coreference: identification chains of noun phrases that refer to the same object. For example, anaphora is a type of coreference.
    • Terminology extraction: finding the relevant terms for a given corpus
    • 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.")
• (Wikipedia, 2009) http://en.wikipedia.org/wiki/Data_extraction
  • Data extraction is the act or process of retrieving (binary) data out of (usually unstructured or badly structured) data sources for further data processing or data storage (data migration). The import into the intermediate extracting system is thus usually followed by data transformation and possibly the addition of metadata prior to export to another stage in the data workflow.
  • Usually, the term data extraction is applied when (experimental) data is first imported into a computer from primary sources, like measuring or recording devices. Today's electronic devices will usually present a electrical connector (e.g. USB) through which 'raw data' can be streamed into a personal computer.
• http://biocreative.sourceforge.net/biocreative_glossary.html
  • Information extraction (IE): IE systems perform natural language text analysis in order to identify information related to pre-defined types of entities (e.g. genes or proteins), relationships, facts or events.

2008

• (Sarawagi, 2008) => Sunita Sarawagi. (2008). "Information extraction. FnT Databases, 1(3), 2008.
  • Information Extraction refers to the automatic extraction of structured information such as entities, relationships between entities, and attributes describing entities from unstructured sources. This enables much richer forms of queries on the abundant unstructured sources than possible with keyword searches alone. When structured and unstructured data co-exist, information extraction makes it possible to integrate the two types of sources and pose queries spanning them.
• (Buitelaar & al, 2008) => Paul Buitelaar, Philipp Cimiano, Anette Frank, Matthias Hartung, and Stefania Racioppa. (2008). "Ontology-based Information Extraction and Integration from Heterogeneous Data Sources." In: International Journal of Human-Computer Studies, 66(11).

2007

• (Banko & al, 2007) => Michele Banko, Michael J. Cafarella, Stephen Soderland, Matt Broadhead, and Oren Etzioni. (2007). "Open Information Extraction from the Web." In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-2007).

2006

• (Paşca & al, 2006) => Marius Paşca, Dekang Lin, Jeffrey Bigham, Andrei Lifchits, and Alpa Jain. (2006). "Names and Similarities on the Web: fact extraction in the fast lane." In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics

2005

• (McCallum, 2005) => Andrew McCallum. (2005). "Information Extraction: Distilling Structured Data from Unstructured Text." In: ACM Queue, 3(9).
  • Notes: It describes the Employment Posting Extraction Task.
• (Mooney & Bunescu, 2005) => Raymond J. Mooney, and Razvan C. Bunescu. (2005). "Mining knowledge from text using information extraction." In: SIGKDD Explorations, 7(1).

2003

• (Grishman, 2003) => Ralph Grishman. (2003). "Information Extraction." In: (Mitkov, 2003).

2001

• (Yangarber, 2001) => R. Yangarber. (2001). "Scenario Customization for Information Extraction. PhD Thesis, New York University.

1997

• (Grishman, 1997) => Ralph Grishman. (1997). "Information extraction: Techniques and challenges. In Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology, International Summer School, (SCIE-97), pages 10–27, 1997.

1999

• (Soderland, 1999) => Stephen Soderland. (1999). "Learning Information Extraction Rules for Semi-Structured and Free Text." In: Machine Learning, 44(1-3). [doi>10.1023/A:1007562322031]
• (Appelt and Israel, 1999) => Douglas E. Appelt and David Israel. (1999). "Introduction to Information Extraction Technology." Tutorial at IJCAI 1999.

1996

• (Strzalkowski & Wang, 1996) => Tomek Strzalkowski, and Jin Wang. (1996). "A Self-Learning Universal Concept Spotter." In: Proceedings of 16th International Conference on Computational Linguistics (COLING 1996).

1995

• David Yarowsky. (1995). "Unsupervised word sense disambiguation rivaling supervised methods." In: Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics, pages 189–196, Cambridge, MA, July 24–26 . ACM Press.

1993

• (Riloff, 2003) => Ellen Riloff (1993). "Automatically Constructing a Dictionary for Information Extraction Tasks." In: Proceedings of AAAI-93.
• (Cardie, 1993) => Claire Cardie. (1993). "A Case-Based Approach to Knowledge Acquisition for Domain-Specific Sentence Analysis." In: Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93).