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

22 Chapter 2. Background Knowledge
the appropriate data. The slots can be, for instance, a table in a relational
database.
2.3.2 Information Extraction Techniques
In order to increase the portability of IE systems, the development of techniques for
IE is currently very centralised on machine learning (Moens, 2006). The alternative
is to adopt approaches based on handcrafted knowledge. Existing IE techniques
may be classified into three groups (Moens, 2006), namely: symbolic techniques,
supervised machine learning and unsupervised machine learning.
All these techniques exploit patterns – recurring sequences of events, evidenced
by the objects in text. Pattern recognition deals with the classification of objects
into categories, according to the values of a selected number of their features. When
using machine learning techniques, after selecting the features to be exploited, extraction
rules are automatically learned from a collection of examples where the
features are annotated. Alternatively, the features might be used for the manual
construction of static rules. According to the types of features to explore, patterns
in text can be classified into the following categories:
• Lexical patterns: features relative to the attributes of lexical items. For
instance, if two lexical items co-occur in a context, or if a word is capitalised
or not. The latter feature is especially productive for NER.
• Syntactic patterns: features include the part-of-speech (POS) of the lexical
items (e.g. noun, verb, preposition), and the type of phrase (e.g. noun phrase,
verb phrase, prepositional phrase).
• Semantic patterns: features that denote semantic classifications in information
units. These include multi-word patterns that may be used for the extraction
of semantic relations, such as works at (e.g. Hugo works at CISUC ), for
denoting an employment relation, or is a (e.g. apple is a fruit) for denoting
hyponymy.
• Discourse patterns: features are values computed by using text fragments,
such as the distance between two entities in a document. It is assumed that
the distance is inversely proportional to the semantic relatedness.
In the rest of this section, we will briefly describe three groups of IE techniques.
Symbolic techniques
This group of IE techniques relies on handcrafted symbolic knowledge. Rules for IE
are written by someone who is familiar with the formalisms of the IE systems and,
especially, familiar with the data where the IE system will run on.
Symbolic techniques are often implemented through partial parsing and finite
state automata (Partee et al., 1990). As the name suggests, partial parsing refers
to situations when only part of the text is analysed, while the rest is skipped.
The analysed part is antecipated through handcrafted patterns, which are in turn
translated into the rules of a finite state automata.