Automatic Content Extraction
Encyclopedia
Automatic Content Extraction (ACE) is a program for developing advanced Information extraction
technologies. Given a text in natural language, the ACE challenge is to detect:
This program began with a pilot study in 1999.
While the ACE program is directed toward extraction of
information from audio and image sources in addition to pure
text, the research effort is restricted to information extraction
from text. The actual transduction of audio and image data into
text is not part of the ACE research effort, although the
processing of ASR and OCR output from such transducers is.
The program relates to English, Arabic and Chinese texts.
The effort involves:
In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it. The ACE program, however, defines the research objectives in terms of the target objects (i.e., the entities, the relations,
and the events) rather than in terms of the words in the
text. For example, the so-called “named entity” task, as
defined in MUC, is to identify those words (on the page)
that are names of entities. In ACE, on the other hand, the
corresponding task is to identify the entity so named. This
is a different task, one that is more abstract and that
involves inference more explicitly in producing an
answer. In a real sense, the task is to detect things that
“aren’t there”.
The ACE corpus is one of the standard benchmarks for testing new information extraction algorithms.
Information extraction
Information extraction is a type of information retrieval whose goal is to automatically extract structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language...
technologies. Given a text in natural language, the ACE challenge is to detect:
- entities mentioned in the text, such as: persons, organizations, locations, facilities, weapons, vehicles, and geo-political entities.
- relations between entities, such as: person A is the manager of company B. Relation types include: role, part, located, near, and social.
- events mentioned in the text, such as: interaction, movement, transfer, creation and destruction.
This program began with a pilot study in 1999.
While the ACE program is directed toward extraction of
information from audio and image sources in addition to pure
text, the research effort is restricted to information extraction
from text. The actual transduction of audio and image data into
text is not part of the ACE research effort, although the
processing of ASR and OCR output from such transducers is.
The program relates to English, Arabic and Chinese texts.
The effort involves:
- defining the research tasks in detail,
- collecting and annotating data needed for training, development, and evaluation,
- supporting the research with evaluation tools and research workshops.
In general objective, the ACE program is motivated by and addresses the same issues as the MUC program that preceded it. The ACE program, however, defines the research objectives in terms of the target objects (i.e., the entities, the relations,
and the events) rather than in terms of the words in the
text. For example, the so-called “named entity” task, as
defined in MUC, is to identify those words (on the page)
that are names of entities. In ACE, on the other hand, the
corresponding task is to identify the entity so named. This
is a different task, one that is more abstract and that
involves inference more explicitly in producing an
answer. In a real sense, the task is to detect things that
“aren’t there”.
The ACE corpus is one of the standard benchmarks for testing new information extraction algorithms.