Comparison of Different Lexical Resources With Respect to the Tip-of-the-Tongue Problem
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Language production is largely a matter of words which, in the case of access problems, can be searched for in an external resource (lexicon, thesaurus). When accessing the resource, the user provides her momentarily available knowledge concerning the target and the resource-powered system responds with the best guess(es) it can make given this input. As tip-of-the-tongue studies have shown, people always have some knowledge concerning the target (meaning fragments, number of syllables, ...) even if its precise or complete form is eluding them. We will show here how to tap on this knowledge to build a resource likely to help authors (speakers/writers) to overcome the Tip-of-the-Tongue (ToT) problem. Yet, before doing so we need a better understanding of the various kinds of knowledge people have when looking for a word. To this end, we asked crowd workers to provide some cues to describe a given target and to specify then how each one of them relates to it, in the hope that this could help others to find the elusive word. Next, we checked how well a given search strategy worked when being applied to differently built lexical networks. The results showed quite dramatic differences, which is not really surprising. After all, different networks are built for different purposes; hence each one of them is more or less well suited for a given task. What was more surprising though is the fact that the relational information given by the users did not allow us to find the elusive word in WordNet more easily than without relying on this information.
Abstract
Language production is largely a matter of words which, in the case of access problems, can be searched for in an external resource (lexicon, thesaurus). When accessing the resource, the user provides her momentarily available knowledge concerning the target and the resource-powered system responds with the best guess(es) it can make given this input. As tip-of-the-tongue studies have shown, people always have some knowledge concerning the target (meaning fragments, number of syllables, ...) even if its precise or complete form is eluding them. We will show here how to tap on this knowledge to build a resource likely to help authors (speakers/writers) to overcome the Tip-of-the-Tongue (ToT) problem. Yet, before doing so we need a better understanding of the various kinds of knowledge people have when looking for a word. To this end, we asked crowd workers to provide some cues to describe a given target and to specify then how each one of them relates to it, in the hope that this could help others to find the elusive word. Next, we checked how well a given search strategy worked when being applied to differently built lexical networks. The results showed quite dramatic differences, which is not really surprising. After all, different networks are built for different purposes; hence each one of them is more or less well suited for a given task. What was more surprising though is the fact that the relational information given by the users did not allow us to find the elusive word in WordNet more easily than without relying on this information.
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