How Do We Count? The Problem of Tagging Phrasal Verbs in Parts

HOW DO WE COUNT? THE PROBLEM OF TAGGING PHRASAL VERBS IN PARTS Nava A. Shaked The Graduate School and University Center The City University of New York 33 West 42nd Street. New York, NY 10036 nava@nynexst.com ABSTRACT This paper examines the current performance of the stochastic tagger PARTS (Church 88) in handling phrasal verbs, describes a problem that arises from the statis- tical model used, and suggests a way to improve the tagger's performance. The solution involves a change in the definition of what counts as a word for the pur- pose of tagging phrasal verbs. 1. INTRODUCTION Statistical taggers are commonly used to preprocess natural language. Operations like parsing, information retrieval, machine translation, and so on, are facilitated by having as input a text tagged with a part of speech label for each lexical item. In order to be useful, a tag- ger must be accurate as well as efficient. The claim among researchers advocating the use of statistics for NLP (e.g. Marcus et al. 92) is that taggers are routinely correct about 95% of the time. The 5% error rate is not perceived as a problem mainly because human taggers disagree or make mistakes at approximately the same rate. On the other hand, even a 5% error rate can cause a much higher rate of mistakes later in processing if the mistake falls on a key element that is crucial to the correct analysis of the whole sentence. One example is the phrasal verb construction (e.g. gun down, back off). An error in tagging this two element sequence will cause the analysis of the entire sentence to be faulty. An analysis of the errors made by the stochastic tag- ger PARTS (Church 88) reveals that phrasal verbs do indeed constitute a problem for the model. 2. PHRASAL VERBS The basic assumption underlying the stochastic pro- cess is the notion of independence. Words are defined as units separated by spaces and then undergo statis- tical approximations. As a result the elements of a phrasal verb are treated as two individual words, each with its own lexical probability (i.e. the probability of observing part of speech i given word j). An interesting pattern emerges when we examine the errors involving phrasal verbs. A phrasal verb such as sum up will be tagged by PARTS as noun + preposition instead of verb + particle. This error influences the tagging of other words in the sentence as well. One typical error is found in infinitive constructions, where a phrase like to gun down is tagged as INTO NOUN IN (a prepo- sitional 'to' followed by a noun followed by another preposition). Words like gun, back, and sum, in iso- lation, have a very high probability of being nouns a.s opposed to verbs, which results in the misclassification described above. However, when these words are fol- lowed by a particle, they are usually verbs, and in the infinitive construction, always verbs. 2.1. THE HYPOTHESIS Tile error appears to follow froln the operation of …

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