Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph. However, such methods are sensitive to the structure of the input synonymy graph: sparseness of the input dictionary can substantially reduce the quality of the extracted synsets. In this paper, we propose two different approaches designed to alleviate the incompleteness of the input dictionaries. The first one performs a pre-processing of the graph by adding missing edges, while the second one performs a post-processing by merging similar synset clusters. We evaluate these approaches on two datasets for the Russian language and discuss their impact on the performance of synset induction methods. Finally, we perform an extensive error analysis of each approach and discuss prominent alternative methods for coping with the problem of sparsity of the synonymy dictionaries.
Meyer and Gurevich @cite_1 presented an approach for construction of an ontologized version of Wiktionary, by formation of ontological concepts and relationships between them from the ambiguous input dictionary, yet their approach does not involve graph clustering.
@cite_1
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The semi-automatic development of ontologies is an important field of research, since existing ontologies often suffer from their small size, unaffordable construction cost, and limited quality of ontology learning systems. The main objective of this chapter is to introduce Wiktionary, which is a collaborative online dictionary encoding information about words, word senses, and relations between them, as a resource for ontology construction. The authors find that a Wiktionary-based ontology can exceed the size of, for example, OpenCyc and OntoWordNet. One particular advantage of Wiktionary is its multilingual nature, which allows the construction of ontologies for different languages. Additionally, its collaborative construction approach means that novel concepts and domain-specific knowledge are quick to appear in the dictionary. For constructing their ontology OntoWiktionary, the authors present a two-step approach that involves (1) harvesting structured knowledge from Wiktionary and (2) ontologizing this knowledge (i.e., the formation of ontological concepts and relationships from the harvested knowledge). They evaluate their approach based on human judgments and find their new ontology to be of overall good quality. To encourage further research in this field, the authors make the final OntoWiktionary publicly available and suggest integrating this novel resource with the linked data cloud as well as other existing ontology projects. This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources. This paper presents an unsupervised method for assembling semantic knowledge from a part-of-speech tagged corpus using graph algorithms. The graph model is built by linking pairs of words which participate in particular syntactic relationships. We focus on the symmetric relationship between pairs of nouns which occur together in lists. An incremental cluster-building algorithm using this part of the graph achieves 82 accuracy at a lexical acquisition task, evaluated against WordNet classes. The model naturally realises domain and corpus specific ambiguities as distinct components in the graph surrounding an ambiguous word. In this paper we present a method for unsupervised semantic role induction which we formalize as a graph partitioning problem. Argument instances of a verb are represented as vertices in a graph whose edge weights quantify their role-semantic similarity. Graph partitioning is realized with an algorithm that iteratively assigns vertices to clusters based on the cluster assignments of neighboring vertices. Our method is algorithmically and conceptually simple, especially with respect to how problem-specific knowledge is incorporated into the model. Experimental results on the CoNLL 2008 benchmark dataset demonstrate that our model is competitive with other unsupervised approaches in terms of F1 whilst attaining significantly higher cluster purity.