A Wordnet from the Ground Up

112 Chapter 4. Extracting Relation Instancesa simple tokeniser and a simple sentence boundary recogniser, rather than advancedtools like the named-entity tagger in Snowball. This relaxation of assumptions madetheir algorithm more general. Patterns are extracted from sentences including seed occurrencesby means of suffix trees for extracting substrings of optimal length. Patternprecision is calculated as the ratio of the correctly matched instance occurrences to allmatches of the pattern. Instances are ordered by the precision of the patterns selectingthem. The process is not iterative.Pantel et al. (2004) proposed an algorithm for mining is-a relations from huge textcorpora. Text is first processed by a part-of-speech tagger (Brill, 1995) and stored ina two-level format: surface word forms and part-of-speech tags. Next, all sentencesincluding seeds are extracted. Patterns are learned from the sentences by calculatingthe minimal edit distance among sentences and registering the edit operations required.Patterns with relatively high occurrence and high precision are identified using the loglikelihoodprinciple (Dunning, 1993) for scoring. Only the 15 highest-score patternshave been used to extract hypernymy instances.Pantel and Pennacchiotti (2006) proposed a system called Espresso which seemsto combine all interesting properties of its predecessors. It does not make any assumptionsconcerning the relation described by the patterns. It works on plain text, usesonly a part-of-speech tagger and a simple chunker, and works iteratively during thesubsequent phases of pattern and instance extraction and evaluation. It is also claimedto be weakly supervised, requiring only the initial set of seeds. Taking into accountthe foregoing selective overview of the previous algorithms and the results of the evaluationof Espresso (Pantel and Pennacchiotti, 2006), we decided to use Espresso as thestarting point for the development of an algorithm that supports the expansion of thecore plWordNet.EspressoEspresso follows the bootstrapping paradigm in a version exemplified already in theSnowball system (Agichtein and Gravano, 2000). Seeds are used to extract the first setof patterns; the subsequent phases of instance and pattern extraction go on automatically.The following four main phases can be identified in Espresso.1. Preprocessing: the input text is divided into tokens (some multiword expressionsare identified) and run through a part-of-speech tagger.2. Pattern induction: sentences including seeds are extracted and patterns arelearned using the algorithm in (Ravichandran and Hovy, 2002).3. Pattern selection: extracted patterns are statistically evaluated and ranked byinstances inducing them; k top patterns are selected.