Machine Learning Approaches

Cimiano et al. (2005) present a machine learning approach for the automatic acquisition of concept hierarchies from English text corpora. The presented re- search is based on the Formal Concept Analysis (FCA), a method based on order theory. The Formal Concept Analysis is mainly used for discovering inherent relationships between objects described through a set of attributes on the one hand, and the attributes themselves on the other. The data are structured into units which are formal abstractions of concepts, allowing meaningful compre- hensible interpretation (Ganter and Wille, 1991). In order to apply the Formal Concept Analysis, Cimiano et al. (2005) analyze their corpus with shallow and deep linguistic analysis tools.

The approach can be describes as follows. First, the corpus is annotated with PoS by the TreeTagger (Schmid, 1994) and parsed using LoPar (Schmid, 2000). From the dependency tree they extract verb-argument dependencies like: verb-subject, verb-object and verb-prepositional phrase. In fact, the verb and the head of the subject, object and prepositional phrase are extracted and lemmatized. In the next step, the extracted lemmatized pairs are statistically weighted and only the

32_{Harris’ distributional hypothesis is that words that occur in the same contexts tend to have}

3.4. ONTOLOGY LEARNING 35

pairs over a given threshold are used for the Formal Concept Analysis. The result of the Formal Concept Analysis is a lattice which transformed into a compacted partial order returns the aimed concept hierarchy.

Cimiano et al. (2005) evaluate their approach from two perspectives. First, they evaluate the extracted concept hierarchy against two existing ontologies from the finance and tourism domain by using the evaluation method proposed by Maedche and Staab (2002). The evaluation method proposed by Maedche and Staab (2002) compares ontologies at three levels: semiotic, syntactic and pragmatic. Cimiano et al. (2005) use this evaluation method in order to measure the lexical and taxonomic overlap between the concept hierarchy extracted with their method and the ones existing in the reference ontologies. The calculated F-measures have shown that the proposed approach performs better for tourism (40.52%) than for the finance domain (33.11%).

The second evaluation Cimiano et al. (2005) perform, is in fact a comparison of their approach with the hierarchical agglomerative clustering33 and Bi-Section- KMeans34. The results of this comparison have shown that the proposed approach by Cimiano et al. (2005) produces better results.

Gamallo et al. (2002) present a machine learning approach for the extraction of semantic relations from a Portuguese text corpus. The method relies on a unsupervised strategy for clustering semantically similar syntactic dependencies. More precisely, the approach allows for the clustering of those syntactic depen- dencies which introduce similar semantic relationships. The research by Gamallo et al. (2002) can be divided into three parts. In the first step, the syntactic de- pendencies are identified in the corpus and clustered in semantic groups. In the next step, the extracted clusters are mapped into semantic roles. In the last step,

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which at the beginning every term forms a cluster of its own.

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the clustered concepts and the relations between these concepts are used for a thesaurus design.

In order to apply the clustering algorithm on syntactic dependencies, Gamallo et al. (2002) tagged their corpus with a PoS tagger (Marques, 2000) and parsed it with a shallow parser (Rocio et al., 2001). Based on this linguistic analysis and a simple heuristics based on right association, Gamallo et al. (2002) select candidates for the dependencies. The candidate dependency between two words implies a relation, and the two words which are potentially related to each other. The two words are in fact the head nouns of the phrases detected by the parser, one being considered the head argument and the other one the complement argu- ment of the relation. Fore example, from the expression fase da evolu¸c˜ao (phase of the evolution) the candidate relation de between fase (in a head position) and evolu¸c˜ao (in the complement position) is extracted.

Having the possible candidates for relation extraction Gamallo et al. (2002) apply the weighted Jaccard coefficient proposed by Gamallo et al. (2001) to measure the semantic similarity between word sets. Gamallo et al. (2002) assume that different arguments of a relation are semantically similar, if they require similar sets of words. This means that they measure the semantic similarity between all head and all complement positions in a relation by comparing their word distri- bution. The clustering algorithm returns for the relation introduced by de and having fase as a head the complement words processo (process), evolu¸c˜ao (execu- tion), investiga¸c˜ao (investigation), trabalho (work) as extensional description of the semantic class required by the head fase. On the other hand, the algorithm clustered for the head position required by the complement evolu¸c˜ao in the re- lation introduced by de the following words: fase (pahse), momento (moment), per´ıodo (period), resultado (result), fin (end).

Based on the clustering results of the candidate relations, Gamallo et al. (2002) consider a candidate relation as a valid relation, if at least one of the two ar-

3.4. ONTOLOGY LEARNING 37

guments (head or complement) are involved in the semantic clustering. For the example presented above, this means that the relation introduced by de between fase (in a head position) and evolu¸c˜ao (in the complement position) is valid. Having all possible relations from the corpus, in the next step Gamallo et al. (2002) define linguistic constraints on the relations in order to map them to the- matic roles. The constraints imply morphology information, syntactic functions as well as information about the determiner of the argument filing the comple- ment position in a relation. The result of this constraints is that for each generic relation, the arguments are mapped into thematic roles. So for example, for the relation introduced by de the head argument is mapped into the semantic role possessed and the complement argument is mapped into the role possession. In the last step of their investigation Gamallo et al. (2002) build an thesaurus based on the clustering and semantic roles. A separate evaluation of the approach is not proposed, but the results of the clustering are integrated in two existing applications. The clustering results are introduced as semantic subcategoriza- tion patterns into the lexicon of the parser used above in order to correct false syntactic attachments proposed by the parser. The results achieved by Gamallo et al. (2002) are also integrated into an Information Retrieval (IR) system for the extension and improvement of documents recall.