Automatic Content Extraction Program

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The Automatic Content Extraction (ACE) Program is a research program intended to motivate the progress of natural language processing tasks.






  • (Bunescu & Mooney, 2007) ⇒ Razvan C. Bunescu, Raymond Mooney. (2007). “Extracting Relations from Text: From Word Sequences to Dependency Paths." In: Anne Kao, and Steve Poteet (eds.) "[ Text Mining and Natural Language
    • Introduction: In this chapter, we present two recent approaches to relation extraction that differ in terms of the kind of linguistic information they use:
      • 1. In the first method (Section 2), each potential relation is represented implicitly as a vector of features, where each feature corresponds to a word sequence anchored at the two entities forming the relationship. A relation extraction system is trained based on the subsequence kernel from [2]. This kernel is further generalized so that words can be replaced with word classes, thus enabling the use of information coming from POS tagging, named entity recognition, chunking or Wordnet [3].
      • 2. In the second approach (Section 3), the representation is centered on the shortest dependency path between the two entities in the dependency path between the two entities in the dependency graph of the sentence. Because syntactic analysis is essential in this method, its applicability is limited to domains where syntactic parsing gives reasonable accuracy.







  • (Harabagiu et al., 2005)
    • Used this dataset to discover twenty four (24) types of relations. E.g. At_located, At_Residence, Role_Staff, Role_Owner, Role_Client, … (I could not find mentions of these in the corpus page. Ask authors).








  • (Maynard et al., 2003) ⇒ Diana Maynard, K. Bontcheva, and Hamish Cunningham. (2003). “Towards a Semantic Extraction of Named Entities.” In: Recent Advances in Natural Language Processing.
    • QUOTE: The ACE program began in September 1999, administered by NSA, NIST, and the CIA. It was designed as \a program to develop technology to extract and characterise meaning from human language". Formal evaluations of ACE algorithm performance are held at approximately 6 month intervals, and are open to all sites who wish to participate, but the results of the evaluations are closed. For this reason we can only publish here details of internal evaluations rather than o cial scores. ACE includes both Entity Detection and Tracking (EDT) and Relation Detection and Characterisation (RDC). EDT is broadly comparable with the MUC Named Entity (NE) task, while RDC is broadly comparable with the MUC template elements task, although both ACE tasks are more challenging than their MUC forerunners.