Hierarchical Preferences in a Broad-Coverage Lexical Taxonomy

Hierarchical Preferences in a Broad-Coverage Lexical Taxonomy Massimiliano Ciaramita (M.Ciaramita@ISTC.CNR.IT) Institute for Cognitive Science and Technology National Research Council, Via Nomentana 56, Roma, 00161 Italy Steven Sloman (Steven Sloman@Brown.Edu) Department of Cognitive and Linguistic Sciences; Box 1978 Mark Johnson (Mark Johnson@Brown.Edu) Department of Cognitive and Linguistic Sciences; Box 1978 Eli Upfal (Eli Upfal@Brown.Edu) Department of Computer Science; Box 1910 Brown University Providence, RI 02912 USA ENTITY Abstract LOCATION We investigate the problem of finding informative su- perordinates in a broad-coverage taxonomy of nominal concepts. We present results from a study which shows that speakers often exhibit strong preferences on what superordinate is more informative, together with a solid bias for specific classes. We then define the task of iden- tifying the properties that characterize such concepts in the taxonomy as a ranking problem. We identify sev- eral such properties which are related to properties of basic concepts. While these properties provide accurate sources of information for identifying the most useful superordinate, their interaction remains obscure. SUBSTANCE ARTIFACT ORGANISM FACILITY ANIMAL CLOTHING FOOD VEHICLE INVERTEBRATE MAMMAL BIRD ASP PLANT CHEMICAL VERTEBRATE FLOWER REPTILE SNAKE FISH TREE TURTLE VIPER RATTLER Introduction Lexical meaning is often summarized as category mem- bership: a “convertible” is a “car”, a “trombonist” is a “musician”, “irritation” is a “feeling”, etc. Within taxonomic organizations a nominal concept belongs to all its superordinates; e.g., “rattler” belongs to “viper”, “snake”, “reptile”, “vertebrate”, “animal”, “organism”. However, certain superordinates such as “snake” tend to be more important than others. Arguably the rele- vance is context-dependent; if a “rattler” was found on an asteroid one would probably wonder how an “ani- mal” managed to get there, more than how a “snake” did. Nonetheless, in hierarchical categorization schemes people tend to prefer useful and efficient classes, in terms of information content, i.e., concepts where the trade-off between size of the category and similarity of its mem- bers is optimal (Gluck & Corter, 1985). Such concepts are called basic (Brown, 1958; Rosch et al. 1976) and are well-studied in humans (Murphy, 2002). In this paper we address two aspects relevant to ba- sic categories. In the first part we investigate for what fraction of nouns in naturally occurring language there is a corresponding “favorite” superordinate. The goal is to estimate, at least as a first crude approximation, the extent of this phenomenon. We examine this issue with a naming task in which we collected data from English speakers concerning their preferences about different su- perordinate levels. We used a broad-coverage nominal Figure 1. A simplified portion of the Wordnet taxonomy of nominal concepts above the noun “rattler”. taxonomy, and several hundred nouns found in a corpus. We found that for a large fraction of nouns, more than 84%, there is a superordinate which is significantly more informative than the others; participants were mostly unsure about nouns referring to abstract concepts such as relations and states. In the second part of the paper we use the outcome of our study as a gold standard and we investigate the properties that characterize informa- tive superordinates. We frame the problem of choosing the “best” superordinate for a noun as a ranking task. We investigate word-specific properties – those that can be extracted from a word’s orthography, from corpus data, and from hierarchical knowledge relating words – to try to characterize informative superordinates. We found that word length provides the weakest pre- dictor, while entropy and frequency, and especially func- tions that measure the association between the noun and the superordinate such as mutual information, are more accurate. Finally, most surprisingly, we found that the best predictor is the concept specificity; i.e., people have a strong preference for specific concepts. This finding suggests a simple hypothesis: that the data might be ex- plained by a model that combines specificity and other