<?xml version="1.0" standalone="yes"?>
<Paper uid="C92-4177">
  <Title>A System for Simpler Construction of Practi-</Title>
  <Section position="8" start_page="0" end_page="0" type="evalu">
    <SectionTitle>
5 Results
</SectionTitle>
    <Paragraph position="0"/>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Tagged Ku~era and Francis Data
</SectionTitle>
      <Paragraph position="0"> The Brown corpus s provided a convenient starting point, since words are tagged for part of speech.</Paragraph>
      <Paragraph position="1"> ltowever, a closer look at the tag set itself reveals a weakness which could bias our results. The tag VBG is used to tag &amp;quot;verb, present participle, and gerund.&amp;quot; Thus, there is no distinction in the label for the different usages of the &amp;quot;-ing&amp;quot; form, although some &amp;quot;-ing&amp;quot; forms are labelled NN for noun or JJ for adjective. Despite this problem, we chose to use the Brown data, knowing that the numbers aright be distorted. We started with a list of the 100 most frequent words labelled as verbs (i.e. the 100 most frequent verbs, which account for over 90verbs which have been discussed in the literature on stativity (such as &amp;quot;resemble&amp;quot;, &amp;quot;matter&amp;quot;, &amp;quot;intend&amp;quot;). Figure Three lists some results, ordered by degree of stativity.</Paragraph>
      <Paragraph position="3"> aPustejovsky, personM communication, hem pointed out some problem cases where the progressive fMsely indicates that root is non-Jtative, such as lie/sit verbs (The book is lying on the shelf, The c~p is sitting on the cownteO, and mental attitude verbs (John il thinking that he should 90 home now, Sdohn is knowing that he should go home note, Marll is suspecting that John will propose tonight. ) Such problemt van be rewolved by fine-tuning testJ.</Paragraph>
      <Paragraph position="4"> degWe thank Slav~ Kate for pointin\[ us to this resource.</Paragraph>
      <Paragraph position="6"> As can be seen, the ranking roughly reflects intuitions about stativity, so, for example, seem is more stative than hear, which is in turn more st~ tire than run.</Paragraph>
      <Paragraph position="7"> Parsing with English Slot Grammar The second more refined method utilizes a parser to analyze text, and to record verb usages. For this purpose, we used the English Slot Grammar (Me-Cord 1980, 19901 a broad-coverage parser written in PKOLOG. 7 To obtain counts of verb usages from the representations produced by ESG, we used a tool for querying trees (QT), built by the second author, also in PROLOG. The corpus is the Reader's Digest (RD) corpus, consisting of just over one million words. We took the same llst of the 115 most frequent and most frequently discussed verbs that was used for obtaining values from the Brown corpus. We extracted all sentences under 30 words containing the inflectional variants of these verbs from the RD corpus. We then ran the parser on this subcorpus, ran QT, and obtained values for the different verb usages. Unlike the Brown data, distinctions are made between the gerundive and participial usages. Figure Four gives results for some verbs, listed in the same order as in Figure Three, with n indicating the number of tokens:</Paragraph>
      <Paragraph position="9"> TAn exception to the quality it imperatives, **here there were some errors in the parsing; they were removed from our cMculationt.</Paragraph>
      <Paragraph position="11"/>
    </Section>
    <Section position="2" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Figure Four
Additional Syntactic Tests
</SectionTitle>
      <Paragraph position="0"> The progressive test is only one of several tests, and in and of itself is certainly inadequate. Several tests mast be run, and then event values must be computed for each linguistic test. Two parameters are involved: the strength of each test as an indicator of e-type, and the sparsity of data.</Paragraph>
      <Paragraph position="1"> We have preliminary results on two tutditional tests: the force/persuade test and the deliberately/carefully test. Synonyms and taxonyms were collected for each (ad)verb, data were extracted frmn the corpus and parsed. For example, the following shows how a sentence with &amp;quot;force&amp;quot; was analyzed. However, more datais needed, from a larger corpus, for the results to be significant.</Paragraph>
      <Paragraph position="2"> The same applies for the adverb test.</Paragraph>
      <Paragraph position="3"> Difficultiue forced him to abandon ...</Paragraph>
      <Paragraph position="4"> verb(force) inf_ camp_verb (abandon) Figure Five - Verb &amp;quot;Force&amp;quot; Results of running and computing the weights of different tests on larger corpora will be reported in future publications.</Paragraph>
    </Section>
  </Section>
class="xml-element"></Paper>