Large-Scale Acquisition of Feature-Based Conceptual Representations from Textual Corpora

Large-Scale Acquisition of Feature-Based Conceptual Representations from Textual Corpora Barry Devereux (barry@csl.psychol.cam.ac.uk) 1 , Nicholas Pilkington (ncvp2@cam.ac.uk) 2 , Thierry Poibeau (thierry.poibeau@ens.fr) 3 , Anna Korhonen (alk23@cam.ac.uk) 2 Centre for Speech, Language and the Brain, Department of Experimental Psychology, University of Cambridge 2 Computer Laboratory, University of Cambridge 3 Laboratoire LaTTiCe-CNRS, Paris Abstract from property norming studies. Currently, the largest set of norms available is that collected by Ken McRae and col- leagues which contains features for 541 concrete concepts (McRae, Cree, Seidenberg, & McNorgan, 2005). Participants listed features for each concept word and McRae et al. nor- malised them by mapping different feature descriptions with the same meaning to the same feature label. Feature-based representations of concepts based on property-norming studies have played an important role in testing theories of conceptual knowledge. However, property norms come with several important caveats (see e.g. Mur- phy, 2002, for a discussion). One issue is that participants tend to under-report features which are present in many of the concepts in a category (McRae et al., 2005; Murphy, 2002, p. 32); for TIGER for example, participants list salient fea- tures like has teeth but not less salient features like has eyes. Thus has eyes is not listed for TIGER although presumably all McRae et al.’s participants knew that tigers have eyes. An- other concern is the size of the currently available property norms. Although the largest collection of norms lists features for over 500 concepts, larger sets of norms would be useful given the number of confounding variables (word length, fa- miliarity, etc) that need to be controlled for in studies of con- cepts and word meaning. Unfortunately, large scale property norming studies are costly and time consuming. In recent years, researchers have begun to develop meth- ods which can automatically extract feature norm-like repre- sentations using corpus-based computational techniques (e.g. Almuhareb & Poesio, 2005; Barbu, 2008; Baroni, Murphy, Barbu, & Poesio, 2009). These approaches – and the ap- proach we present in this paper – have their antecedents in early methods for extracting and organizing the seman- tic feature information implicit in dictionary definitions (e.g. Chodorow, Byrd, & Heidorn, 1985). The automatic approach is cost-effective and can gather large-scale frequency data from text corpora. As corpora contain words denoting con- cepts and their features in natural language, they provide ideal material for feature generation. However, current methods target concept-feature tuples only or are restricted to specific relations between concepts and their features. For example, Almuhareb and Poesio (2005) targeted is-a and part-of rela- tions, whilst Barbu (2008) combined linguistic patterns with a co-occurrence based method to extract six types of features: superordinate, part, stuff, location, quality and action. The Strudel model (Baroni et al., 2009) also uses linguis- Methods for estimating people’s conceptual knowledge have the potential to be very useful to theoretical research on con- ceptual semantics. Traditionally, feature-based conceptual rep- resentations have been estimated using property norm data; however, computational techniques have the potential to build such representations automatically. The automatic acquisition of feature-based conceptual representations from corpora is a challenging task, given the unconstrained nature of what can constitute a semantic feature. Existing computational methods typically do not target the full range of concept-relation-feature triples occurring in human generated norms (e.g. tiger have stripes) but rather focus on concept-feature tuples (e.g. tiger – stripes) or triples involving specific relations only. We in- vestigate the large-scale extraction of concept-relation-feature triples and the usefulness of encyclopedic, syntactic and se- mantic information in guiding the extraction process. Our method extracts candidate triples (e.g. tiger have stripes, flute produce sound) from parsed corpus data and ranks them on the basis of semantic information. Our investigation shows the usefulness of external knowledge in guiding feature ex- traction and highlights issues of methodology and evaluation which need to be addressed in developing models for this task. Keywords: distributed conceptual representations; semantic features; corpus-based acquisition Introduction Concrete concepts like TIGER , APPLE and CHISEL constitute a fundamental part of people’s coherent mental representa- tions of the world around them. A key question in cogni- tive science is how these semantic representations are organ- ised and accessed. Most theories of conceptual representa- tion assume a distributed, feature-based model of conceptual knowledge (e.g. Cree, McNorgan, & McRae, 2006; Randall, Moss, Rodd, Greer, & Tyler, 2004; Tyler, Moss, Durrant- Peatfield, & Levy, 2000). According to such theories, con- ceptual knowledge is distributed across a network of intercon- nected feature units (such as has eyes, has ears, has stripes) with concepts’ meanings being represented as patterns of ac- tivation across these units. The relative prominence of this distributed, feature-based account of conceptual representa- tion in the literature reflects the many perceived strengths of such a framework. A key issue for all studies which aim to test distributed theories of concepts is the accurate estimation of the knowl- edge that people are likely to represent in such a system. Re- cent connectionist, behavioural and neuropsychological stud- ies (e.g. Cree et al., 2006; Grondin, Lupker, & McRae, 2009; Randall et al., 2004; Tyler et al., 2000; Taylor, Salamoura, Randall, Moss, & Tyler, 2008) have relied on data derived