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Preliminary Recommendations

Practical NLP lexicons

In this section an overview of the following practical lexicons for NLP is provided:

The lexicons differ largely as to the number of entries they contain and to the amount of linguistic information each entry is provided with: while some lexicons attach more importance to coverage, others appear to concentrate on granularity and depth of lexical representations often to the detriment of breadth. This is mainly due to the fact that most such lexicons were aimed at a wide variety of different applications (ranging from machine translation to style-checking). Moreover, some are strongly biased towards specific linguistic theories. For the sake of concreteness, a specific case study was chosen as a basis for comparison between these lexicons: the encoding of verb entries and, particularly, of their subcategorisation frame. Although this area has been the focus of intense research over the last fifteen years, it still rests on fairly shaky ground.

In what follows, we will list all aspects which appear to be relevant for the encoding of subcategorisation and consider them in more detail. It should be noted that not all of the following are pertinent to all lexicons, as will be indicated below:

The division between the top of this list and the bottom is to be interpreted as indicative of a scale from the least to the most uniformly-distributed properties: in other words, the properties in the topmost part of the list are shared by all lexicons, the others are shared only by some and with considerable differences in the way these properties are represented therein. In what follows, we will first sketchily characterise the approach to verb subcategorisation that each lexicon takes, then move on to a more detailed consideration of the above listed aspects.





next up previous contents
Next: Approaches to verb subcategorisation Up: Comparing approaches to subcategorisation Previous: A comparison