Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...

2.3. Information Extraction from Text 21
2.3 Information Extraction from Text
Information extraction (IE, see Moens (2006) for an extensive overview) consists
of the identification and classification of information in unstructured data sources,
which this way becomes structured and ready, for instance, for populating a relational
database and for being used directly by computational applications. In the
specific case of IE from text, the target is text, written in natural language.
Although they are both solutions to the information overload problem, IE should
not be confused with information retrieval (IR) (Baeza-Yates and Ribeiro-Neto,
1999), which is the task of locating required information within collections of data.
In IR, the information to be searched is specified by a query, which can be, for
instance, a group of keywords or a natural language question. The retrieved information
is often a list of relevant documents, according to the query, which should
thus contain the required information.
2.3.1 Tasks in Information Extraction from Text
According to Jurafsky and Martin (2009), a complete system for IE from text has
typically four steps, where it performs the tasks of named entity recognition (NER),
relation detection and classification, temporal event processing, and template filling:
1. NER (Chinchor and Robinson, 1997; Mota and Santos, 2008) is the task of
identifying proper names mentioned in text. It can include the classification
of the entities, which consists of attributing a category and, sometimes, a
sub-category, to the entities, from a range including, but often not limited
to, people, organizations and places. Moreover, as the entities are not always
mentioned by the same name, and are sometimes referred by a pronoun, the
task of NER might as well need to deal with coreference and anaphora resolution
(Mitkov et al., 2000; Recasens et al., 2010). In our work, we are more
interested in the identification of lexical entities, and not named entities.
2. Relation detection and classification (Hendrickx et al., 2010) is closely
related to the scope of this thesis, and is the task of identifying semantic
relations among the discovered entities, including, but not limited to, the ones
presented in section 2.1.2. Semantic relations between named entities include,
but are not limited to, family, employment or geospatial relations (see more
in Freitas et al. (2009)).
3. As some of the relations might be true or false for different periods of time, it
is sometimes important to determine when the events in the text happened.
Temporal event processing (Verhagen et al., 2010) is related to the analysis
of time expressions which include, for instance: mentions of the days of
the week or months (e.g. Sunday or February), names of special days (e.g.
Christmas, Valentine’s Day), relative expressions (e.g. in two months, next
year), clock and calendar times (e.g. 17:00 P.M., 2012-09-25 ). This task is
however out of the scope of this thesis.
4. Template filling is the task of searching for required data in documents
that describe stereotypical information and then filling predefined slots with