Categorizing words using 'frequent frames': what cross-linguistic analyses reveal about distributional acquisition strategies

Abstract

Mintz (2003) described a distributional environment called a frame, defined as the co-occurrence of two context words with one intervening target word. Analyses of English child-directed speech showed that words that fell within any frequently occurring frame consistently belonged to the same grammatical category (e.g. noun, verb, adjective, etc.). In this paper, we first generalize this result to French, a language in which the function word system allows patterns that are potentially detrimental to a frame-based analysis procedure. Second, we show that the discontinuity of the chosen environments (i.e. the fact that target words are framed by the context words) is crucial for the mechanism to be efficient. This property might be relevant for any computational approach to grammatical categorization. Finally, we investigate a recursive application of the procedure and observe that the categorization is paradoxically worse when context elements are categories rather than actual lexical items. Item-specificity is thus also a core computational principle for this type of algorithm. Our analysis, along with results from behavioural studies (Gómez, 2002; Gómez and Maye, 2005; Mintz, 2006), provides strong support for frames as a basis for the acquisition of grammatical categories by infants. Discontinuity and item-specificity appear to be crucial features.

Figure 1

Accuracy for the largest groups obtained from frames. From left to right, accuracy is reported for the largest group, the set composed by the 2 largest groups, the set composed by the 3 largest groups and so on; numbers on the horizontal axis represent the minimal number of types classified for each group included in the result.

Figure 2

Completeness for the largest groups obtained from frames (see figure 1 for details about the groups selected).

Figure 3

A crucial role for discontinuity: Trees in (1) illustrate the fact that an adjacent pair of words in close syntactic relation could be embedded into virtually infinitely many structures (many more structures could be constructed if we lifted the constraint that A and B are immediate sisters, but we suppose that they are in close syntactic relationship since they co-occur frequently). Thus, the following -or preceding- syntactic position is not constrained very much: this may explain low accuracy results of adjacent contexts (Experiment 2). Trees in (2), in contrast, show that when A and B are not adjacent (but still syntactically close), only two positions remain theoretically available for an intervening word. This may account for the fact that discontinuity appears to be an essential feature of the success of the frequent frames algorithm (even though adjacent contexts appear to be computationally equivalent, at first sight).

Equation 1