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<Paper uid="J93-1005">
  <Title>Structural Ambiguity and Lexical Relations</Title>
  <Section position="3" start_page="0" end_page="106" type="metho">
    <SectionTitle>
2. Discovering Lexical Association in Text
</SectionTitle>
    <Paragraph position="0"> A 13 million-word sample of Associated Press news stories from 1989 were automatically parsed by the Fidditch parser (Hindle 1983 and in press), using Church's  Donald Hindle and Mats Rooth Structural Ambiguity and Lexical Relations Table 1 A sample of NP heads, preceding verbs, and following prepositions derived from the parsed corpus.</Paragraph>
    <Paragraph position="1">  a. change in -V b. regulation c. aim PRO-+ at d. remedy shortage of e. good in f. DART-PNP g. assuage citizen h. scarcity of i. item as j. wiper k. VING 1. VING  part-of-speech analyzer as a preprocessor (Church 1988), a combination that we will call simply &amp;quot;the parser.&amp;quot; The parser produces a single partial syntactic description of a sentence. Consider Example 2, and its parsed representation in Example 3. The information in the tree representation is partial in the sense that some attachment information is missing: the nodes dominated by &amp;quot;?&amp;quot; have not been integrated into the syntactic representation. Note in particular that many PPs have not been attached. This is a symptom of the fact that the parser does not (in many cases) have the kind of lexical information that we have just claimed is required in resolving PP attachment. Example 2 The radical changes in export and customs regulations evidently are aimed at remedying an extreme shortage of consumer goods in the Soviet Union and assuaging citizens angry over the scarcity of such basic items as soap and windshield wipers. From the syntactic analysis provided by the parser, we extracted a table containing the heads of all noun phrases. For each noun phrase head, we recorded the following preposition if any occurred (ignoring whether or not the parser had attached the preposition to the noun phrase), and the preceding verb if the noun phrase was the object of that verb. The entries in Table 1 are those generated from the text above. Each noun phrase in Example 3 is associated with an entry in the Noun column of the table. Usually this is simply the root of the head of the noun phrase: good is the root of the head of consumer goods. Noun phrases with no head, or where the head is not a common noun, are coded in a special way: DARTopNP represents a noun phrase beginning with a definite article and headed by a proper noun, and VING represents a gerundive noun phrase. PRO-+ represents the empty category which, in the syntactic theory underlying the parser, is assumed to be the object of the passive verb aimed. In cases where a prepositional phrase follows the noun phrase, the head preposition appears in the Prep column; attached and unattached prepositional phrases generate the same kinds of entries. If the noun phrase is an object, the root of the governing verb appears in the Verb column: aim is the root of aimed, the verb governing the empty</Paragraph>
    <Paragraph position="3"> Donald Hindle and Mats Rooth Structural Ambiguity and Lexical Relations category \[fRo +\]. The last column in the table, labeled Syntax, marks with the symbol -V all cases where there is no preceding verb that might license the preposition: the initial subject of Example 2 is such a case.</Paragraph>
    <Paragraph position="4"> In the 13 million-word sample, 2,661,872 noun phrases were identified. Of these, 467,920 were recognized as the object of a verb, and 753,843 were followed by a preposition. Of the object noun phrases identified, 223,666 were ambiguous verb-noun-preposition triples.</Paragraph>
  </Section>
  <Section position="4" start_page="106" end_page="110" type="metho">
    <SectionTitle>
3. Estimating Associations
</SectionTitle>
    <Paragraph position="0"> The table of verbs, nouns, and prepositions is in several respects an imperfect source of information about lexical associations. First, the parser gives us incorrect analyses in some cases. For instance, in the analysis partially described in Example 4a, the parser incorrectly classified probes as a verb, resulting in a table entry probe lightning in. Similarly, in Example 4b, the infinitival marker to has been misidentified as a preposition.</Paragraph>
    <Paragraph position="1"> Example 4 a. \[NpThe space\] \[w~s probes\] \[Npdetected lightning\] \[pp in Jupiter's upper atmosphere\] and observed auroral emissions like Earth's northern lights in the Jovian polar regions.</Paragraph>
    <Paragraph position="2"> b. The Bush administration told Congress on Tuesday it wants to \[v preserve\] \[Npthe right\] \[~ \[~ to\] control entry\] to the United States of anyone who was ever a Communist.</Paragraph>
    <Paragraph position="3"> Second, a preposition in an entry might be structurally related to neither the noun of the entry nor the verb (if there is one), even if the entry is derived from a correct parse.' For instance, the phrase headed by the preposition might have a higher locus of attachment: Example 5 a. The Supreme Court today agreed to consider reinstating the murder conviction of a New York City man who confessed to \[v,NG killing\] \[Nrhis former girlfriend\] \[p after\] police illegally arrested him at his home. b. NBC was so afraid of hostile advocacy groups and unnerving advertisers that it shot its dramatization of the landmark court case that \[VPAST legalized\] \[Nrabortion\] \[Pr under two phony script titles\] The temporal phrase headed by after modifies confess, but given the procedure described above, Example 5a results in a tuple kill girlfriend after. In the second example, a tuple legalize abortion under is extracted, although the PP headed by under modifies the higher verb shot.</Paragraph>
    <Paragraph position="4"> Finally, entries of the form verb noun preposition do not tell us whether to induce a lexical association between verb and preposition or between noun and preposition. We will view the first two problems as noise that we do not have the means to eliminate, 1 For present purposes, we can consider a parse correct if it contains no incorrect information in the relevant area. Provided the PPs in Example 5 are unattached, the parses would be correct in this sense. The incorrect information is added by our table construction step, which (given our interpretation of the table) assumes that a preposition following an object NP modifies either the NP or its governing verb.  Computational Linguistics Volume 19, Number 1 and partially address the third problem in a procedure we will now describe. We want to use the verb-noun-preposition table to derive a table of bigrams counts, where a bigram is a pair consisting of a noun or verb and an associated preposition (or no preposition). To do this we need to try to assign each preposition that occurs either to the noun or to the verb that it occurs with. In some cases it is fairly certain whether the preposition attaches to the noun or the verb; in other cases, this is far less certain. Our approach is to assign the clear cases first, then to use these to decide the unclear cases that can be decided, and finally to divide the data in the remaining unresolved cases between the two hypotheses (verb and noun attachment). The procedure for assigning prepositions is as follows:</Paragraph>
    <Paragraph position="6"> No Preposition--if there is no preposition, the noun or verb is simply entered with a special symbol NULL, conceived of as the null preposition. (Items b, f, g, and j-1 in Table 1 are assigned).</Paragraph>
    <Paragraph position="7"> Sure Verb Attach 1--the preposition is attached to the verb if the noun phrase head is a pronoun.</Paragraph>
    <Paragraph position="8"> Sure Verb Attach 2---the preposition is attached to the verb if the verb is passivized, unless the preposition is by. The instances of by following a passive verb were left unassigned. (Item c in Table 1 is assigned).</Paragraph>
    <Paragraph position="9"> Sure Noun Attach--the preposition is attached to the noun, if the noun phrase occurs in a context where no verb could license the prepositional phrase, specifically if the noun phrase is in a subject or other pre-verbal position. The required syntactic information is present in the last column of the table derived from the parse. (Item a in Table 1 is assigned.) Ambiguous Attach 1--Using the table of attachments computed so far, if the LA-score for the ambiguity (a score that compares the probability of noun versus verb attachment, as described below) is greater than 2.0 or less than -2.0, then assign the preposition according to the LA-score.</Paragraph>
    <Paragraph position="10"> Iterate until this step produces no new attachments. (Item d in Table 1 may be assigned.) Ambiguous Attach 2--for the remaining ambiguous triples, split the datum between the noun and the verb, assigning a count of .5 to the noun-preposition pair and .5 to the verb-preposition pair. (Item d in Table 1 is assigned, if not assigned in the previous step.) Unsure Attach--assign remaining pairs to the noun. (Items e, h, and i in Table 1 are assigned.) This procedure gives us bigram counts representing the frequency with which a given noun occurs associated with an immediately following preposition (or no preposition), or a given verb occurs in a transitive use and is associated with a preposition immediately following the object of the verb. We use the following notation: f(w, p) is the frequency count for the pair consisting of the verb or noun w and the preposition p. The unigram frequency count for the word w (either a verb, noun, or preposition) can be viewed as a sum of bigram frequencies, and is written f(w). For instance, if p is a preposition, f(p) = ~wf~W, p).</Paragraph>
    <Paragraph position="11">  Donald Hindle and Mats Rooth Structural Ambiguity and Lexical Relations</Paragraph>
    <Section position="1" start_page="108" end_page="108" type="sub_section">
      <SectionTitle>
3.1 The Procedure for Guessing Attachment
</SectionTitle>
      <Paragraph position="0"> Our object is to develop a procedure to guess whether a preposition is attached to the verb or its object when a verb and its object are followed by a preposition. We assume that in each case of attachment ambiguity, there is a forced choice between two outcomes: the preposition attaches either to the verb or to the noun. 2 For example, in Example 6, we want to choose between two possibilities: either into is attached to the verb send or it is attached to the noun soldier.</Paragraph>
      <Paragraph position="1"> Example 6 Moscow sent more than 100,000 soldiers into Afghanistan ...</Paragraph>
      <Paragraph position="2"> In particular, we want to choose between two structures:</Paragraph>
    </Section>
    <Section position="2" start_page="108" end_page="110" type="sub_section">
      <SectionTitle>
Example 7
</SectionTitle>
      <Paragraph position="0"> a. verb_attach structure: \[vpsend \[NP&amp;quot; soldier NULL\] \[pp into ..\] ..\] b. noun_attach structure: \[vpsend \[NP&amp;quot; soldier \[Fpinto ..\]\] .. \] For the verb_attach case, we require not only that the preposition attach to the verb send but also that the noun soldier have no following prepositional phrase attached: since into directly follows the head of the object noun phrase, there is no room for any post-modifier of the noun soldier. We use the notation NULL to emphasize that in order for a preposition licensed by the verb to be in the immediately postnominal position, the noun must have no following complements (or adjuncts). For the case of noun attachment, the verb may or may not have additional prepositional complements following the prepositional phrase associated with the noun.</Paragraph>
      <Paragraph position="1"> Since we have a forced choice between two outcomes, it is appropriate to use a likelihood ratio to compare the attachment probabilities (cf. Mosteller and Wallace 1964). 3 In particular, we look at the log of the ratio of the probability of verb_attach to the probability of noun_attach. We will call this log likelihood ratio the LA (lexical association) score. P(verb~ttach p \[ v, n) LA(v, n,p) = log 2 P(noun_attach p \[ v, n) For the current example, P(verb_attach into \[ sendv, soldierN) ,~ P( into\[sendv ) * P(NULL\[soldierN) and P(noun_attach into \[ sendv, soldierN) ~, P(into\[soldierN).</Paragraph>
      <Paragraph position="2"> Again, the probability of noun attachment does not involve a term indicating that the verb sponsors no (additional) complement; when we observe a prepositional phrase that is in fact attached to the object NP, the verb might or might not have a complement or adjunct following the object phrase.</Paragraph>
      <Paragraph position="3"> 2 Thus we are ignoring the fact that the preposition may in fact be licensed by neither the verb nor the noun, as in Example 5.</Paragraph>
      <Paragraph position="4"> 3 In earlier versions of this paper we used a t-test for deciding attachment and a different procedure for estimating the probabilities. The current procedure has several advantages. Unlike the t-test used previously, it is sensitive to the magnitude of the difference between the two probabilities, not to our confidence in our ability to estimate those probabilities accurately. And our estimation procedure has the property that it defaults (in case of novel words) to the average behavior for nouns or verbs, for instance, reflecting a default preference with of for noun attachment.</Paragraph>
      <Paragraph position="5">  Computational Linguistics Volume 19, Number 1 We can estimate these probabilities from the table of co-occurrence counts as: 4</Paragraph>
      <Paragraph position="7"> Thus, the LA score for this example is: LA ( sendv , soldierN , into) = log 2 .049 *.800 .0007 5.81 The LA score has several useful properties. The sign indicates which possibility, verb attachment or noun attachment, is more likely; an LA score of zero means they are equally likely. The magnitude of the score indicates how much more probable one outcome is than the other. For example, if the LA score is 2.0, then the probability of verb attachment is four times greater than noun attachment. Depending on the task, we can require a certain threshold of LA score magnitude before making a decision) As usual, in dealing with counts from corpora we must confront the problem of how to estimate probabilities when counts are small. The maximum likelihood estimate described above is not very good when frequencies are small, and when frequencies are zero, the formula will not work at all. We use a crude adjustment to observed frequencies that has the right general properties, though it is not likely to be a very good estimate when frequencies are small. For our purposes, however exploring in general the relation of distribution in a corpus to attachment disambiguation--we believe it is sufficient. Other approaches to adjusting small frequencies are discussed in Church et al. (1991) and Gale, Church, Yarowsky (in press).</Paragraph>
      <Paragraph position="8"> The idea is to use the typical association rates of nouns and verbs to interpolate our probabilities. Where f(N, p) = En f(n, p), f(V, p) = ~vf(V, p), f(N) -- End(n) and  framework to take into account other factors that might influence our decision about attachment (see Gale, Church, and Yarowsky \[in press\] for a discussion of this approach). We know of course that other information has a bearing on the attachment decision. For example, we have observed that if the noun phrase object includes a superlative adjective as a premodifier, then noun attachment is certain (for a small sample of 16 cases). We could easily take this into account by setting the prior odds ratio to heavily favor noun attachment: let's suppose that if there is a superlative in the object noun phrase, then noun attachment is say 1000 times more probable than verb attachment; otherwise, they are equally probable. Then following Mosteller and Wallace (1964), we assume that Final attachment odds = log 2(initial odds) + LA.</Paragraph>
      <Paragraph position="9"> In case there is no superlative in the object, the initial log odds will be zero (verb and noun attachment are equally probable), and the final odds will equal our LA score. If there is a superlative,  Donald Hindle and Mats Rooth Structural Ambiguity and Lexical Relations</Paragraph>
      <Paragraph position="11"> When f(n) is zero, the estimate for P(p I n) is the average (~) across all nouns, f(N) and similarly for verbs. When f(n, p) is zero, the estimate used is proportional to this average. If we have seen only one case of a noun and it occurred with a preposition p (that is f(n, p) = 1 and f(n) = 1 ), then our estimate is nearly cut in half. This is the kind of effect we want, since under these circumstances we are not very confident in 1 as an estimate of P(p I n). When f(n, p) is large, the adjustment factor does not make much difference. In general; this interpolation procedure adjusts small counts in the right direction and has little effect when counts are large.</Paragraph>
      <Paragraph position="12"> For our current example, this estimation procedure changes the LA score little:</Paragraph>
      <Paragraph position="14"> = 5.87.</Paragraph>
      <Paragraph position="15"> The LA score of 5.87 for this example is positive and therefore indicates verb attachment; the magnitude is large enough to suggest a strong preference for verb attachment. This method of calculating the LA score was used both to decide unsure cases in building the bigram tables as described in Ambiguous Attach 1, and to make the attachment decisions in novel ambiguous cases, as discussed in the sections following.</Paragraph>
    </Section>
  </Section>
  <Section position="5" start_page="110" end_page="117" type="metho">
    <SectionTitle>
4. Testing Attachment
</SectionTitle>
    <Paragraph position="0"> To evaluate the performance of the procedure, 1000 test sentences in which the parser identified an ambiguous verb-noun-preposition triple were randomly selected from AP news stories. These sentences were selected from stories included in the 13 million-word sample, but the particular sentences were excluded from the calculation of lexical associations. The two authors first guessed attachments on the verb-noun-preposition triples, making a judgment on the basis of the three headwords alone. The judges were required to make a choice in each instance. This task is in essence the one that we will give the computer--to judge the attachment without any more information than the preposition and the heads of the two possible attachment sites.</Paragraph>
    <Paragraph position="1"> This initial step provides a rough indication of what we might expect to be achievable based on the information our procedure is using. We also wanted a standard of correctness for the test sentences. We again judged the attachment for the 1000 triples,  Computational Linguistics Volume 19, Number 1 this time using the full-sentence context, first grading the test sentences separately, and then discussing examples on which there was disagreement. Disambiguating the test sample turned out to be a surprisingly difficult task. While many decisions were straightforward, more than 10% of the sentences seemed problematic to at least one author. There are several kinds of constructions where the attachment decision is not clear theoretically. These include idioms as in Examples 8 and 9, light verb constructions (Example 10), and small clauses (Example 11).</Paragraph>
    <Paragraph position="2">  In the case of idioms, we made the assignment on the basis of a guess about the syntactic structure of the idiom, though this was sometimes difficult to judge. We chose always to assign light verb constructions to noun attachment, based on the fact that the noun supplies the lexical information about what prepositions are possible, and small clauses to verb attachment, based on the fact that this is a predicative construction lexically licensed by the verb.</Paragraph>
    <Paragraph position="3"> Another difficulty arose with cases where there seemed to be a systematic semantically based indeterminacy about the attachment. In the situation described by Example 12a, the bar and the described event or events are presumably in the same location, and so there is no semantic reason to decide on one attachment. Example 12b shows a systematic benefactive indeterminacy: if you arrange something for someone, then the thing arranged is also for them. The problem in Example 12c is that signing an agreement usually involves two participants who are also parties to the agreement.  Example 13 gives some further examples drawn from another test sample.</Paragraph>
    <Paragraph position="4"> Example 12 a .... known to frequent the same bars in one neighborhood.</Paragraph>
    <Paragraph position="5">  b. Inaugural officials reportedly were trying to arrange a reunion for Bush and his old submarine buddies ...</Paragraph>
    <Paragraph position="6"> c. We have not signed a settlement agreement with them.</Paragraph>
    <Paragraph position="7"> Example 13 a. It said the rebels issued a challenge to soldiers in the area and fought with them for 30 minutes.</Paragraph>
    <Paragraph position="8"> b. The worst such attack came Nov. 11 when a death squad firing submachine guns killed 43 people in the northwest town of Segovia.</Paragraph>
    <Paragraph position="9"> c. Another charge raised at the Contra news conference was that the Sandinistas have mined roads along the Honduran and Costa Rican borders.</Paragraph>
    <Paragraph position="10">  Donald Hindle and Mats Rooth Structural Ambiguity and Lexical Relations d. Buckner said Control Data is in the process of negotiating a new lending agreement with its banks.</Paragraph>
    <Paragraph position="11"> e .... which would require ailing banks and S&amp;Ls to obtain advance permission from regulators before raising high-cost deposits through money brokers.</Paragraph>
    <Paragraph position="12"> f. She said the organization has opened a cleaning center in Seward and she was going to Kodiak to open another.</Paragraph>
    <Paragraph position="13"> In general, we can say that an attachment is semantically indeterminate if situations that verify the meaning associated with one attachment also make the meaning associated with the other attachment true. Even a substantial overlap (as opposed to identity) between the classes of situations verifying the two meanings makes an attachment choice difficult.</Paragraph>
    <Paragraph position="14"> The problems in determining attachments are heterogeneous. The idiom, light verb, and small clause constructions represent cases where the simple distinction between noun attachment and verb attachment perhaps does not make sense, or is very theory-dependent. It seems to us that the phenomenon of semantically based indeterminacy deserves further exploration. If it is often difficult to decide what licenses a prepositional phrase, we need to develop language models that appropriately capture this. For our present purpose, we decided to make an attachment choice in all cases, in some cases relying on controversial theoretical considerations, or relatively unanalyzed intuitions.</Paragraph>
    <Paragraph position="15"> In addition to the problematic cases, 120 of the 1000 triples identified automatically as instances of the verb-object-preposition configuration turned out in fact to be other constructions, often as the result of parsing errors. Examples of this kind were given above, in the context of our description of the construction of the verb-noun-preposition table. Some further misidentifications that showed up in the test sample are: identifying the subject of the complement clause of say as its object, as in Example 10, which was identified as (say ministers from), and misparsing two constituents as a single-object noun phrase, as in Example 11, which was identified as (make subject to).  Example 14 a. Ortega also said deputy foreign ministers from the five governments would meet Tuesday in Managua, ...</Paragraph>
    <Paragraph position="16"> b. Congress made a deliberate choice to make this commission subject to the open meeting requirements ...</Paragraph>
    <Paragraph position="17"> After agreeing on the 'correct' attachment for the test sample, we were left with 880 disambiguated verb-noun-preposition triples, having discarded the examples that were not instances of the relevant construction. Of these, 586 are noun attachments and 294 are verb attachments.</Paragraph>
    <Section position="1" start_page="112" end_page="115" type="sub_section">
      <SectionTitle>
4.1 Evaluating Performance
</SectionTitle>
      <Paragraph position="0"> First, consider how the simple structural attachment preference schemas perform at predicting the outcome in our test set. Right Association predicts noun attachment and does better, since in our sample there are more noun attachments, but it still has an error rate of 33%. Minimal Attachment, interpreted as entailing verb attachment, has the complementary error rate of 67%. Obviously, neither of these procedures is particularly impressive.</Paragraph>
      <Paragraph position="1">  Now consider the performance of our lexical association (LA) procedure for the 880 standard test sentences. Table 2 shows the performance for the two human judges and for the lexical association attachment procedure. First, we note that ~he task of judging attachment on the basis of verb, noun, and preposition alone is not easy. The figures in the entry labeled &amp;quot;combined precision&amp;quot; indicate that the human judges had overall error rates of 12-15%. 6 The lexical association procedure is somewhat worse than the human judges, with an error rate of 20%, but this is an improvement over the structural strategies.</Paragraph>
      <Paragraph position="2"> The table also gives results broken down according to N vs. V attachment. The precision figures indicate the proportion of test items assigned to a given category that actually belong to the category. For instance, N precision is the fraction of cases that the procedure identified as N attachments that actually were N attachments. The recall figures indicate the proportion of test items actually belonging to a given category that were assigned to that category: N precision is the fraction of actual N attachments that were identified as N attachments. The LA procedure recognized about 85% of the 586 actual noun attachment examples as noun attachments, and about 70% of the actual verb attachments as verb attachments.</Paragraph>
      <Paragraph position="3"> If we restrict the lexical association procedure to choose attachment only in cases where the absolute value of the LA score is greater than 2.0 (an arbitrary threshold indicating that the probability of one attachment is four times greater than the other), we get attachment judgments on 621 of the 880 test sentences, with overall precision of about 89%. On these same examples, the judges also showed improvement, as evident in Table 3. 7 6 Combined precision is the proportion of assigned items that were correctly assigned. Combined recall is the proportion of all items that were correctly assigned. These differ if some items are left unassigned, as in Table 3. 7 The recall figures for the judges do not have the usual interpretation, since the judges did not have the option of passing on test items. The ratios given in parentheses indicate the proportion of items in the relevant category that were both correctly labeled by the judge and had a likelihood ratio score with absolute value greater than 2.0. Thus these figures are of interest only in relation to the recall figures for LA.</Paragraph>
      <Paragraph position="4">  The fact that an LA score threshold improves precision indicates that the LA score gives information about how confident we can be about an attachment choice. In some applications, this information is useful. For instance, suppose that we wanted to incorporate the PP attachment procedure in a parser such as Fidditch. It might be preferable to achieve increased precision in PP attachment, in return for leaving some PPs unattached. For this purpose, a threshold could be used. Table 4 shows the combined precision and recall levels at various LA thresholds. It is clear that the LA score can be used effectively to trade off precision and recall, with a floor for the forced choice at about 80%.</Paragraph>
      <Paragraph position="5"> A comparison of Table 3 with Table 2 indicates, however, that the decline in recall is severe for V attachment. And in general, the performance of the LA procedure is worse on V attachment examples than on N attachments, according to both precision and recall criteria. The next section is concerned with a classification of the test examples, which gives insight into why performance on V attachments is worse.</Paragraph>
    </Section>
    <Section position="2" start_page="115" end_page="117" type="sub_section">
      <SectionTitle>
4.2 Underlying Relations
</SectionTitle>
      <Paragraph position="0"> Our model takes frequency of co-occurrence as evidence of an underlying relationship but makes no attempt to determine what sort of relationship is involved. It is interesting to see what kinds of relationships are responsible for the associations the model is identifying. To investigate this we categorized the 880 triples according to the nature of the relationship underlying the attachment. In many cases, the decision was difficult. The argument/adjunct distinction showed many gray cases between clear participants in an action and clear adjuncts, such as temporal modifiers. We made rough best guesses to partition the cases into the following categories: argument, adjunct, idiom, small clause, systematic locative indeterminacy, other systematic indeterminacy, and light verb. With this set of categories, 78 of the 880 cases remained so problematic that we assigned them to the category other.</Paragraph>
      <Paragraph position="1"> Table 5 shows the proportion of items in a given category that were assigned the correct attachment by the lexical association procedure. Even granting the roughness of the categorization, some clear patterns emerge. Our approach is most successful at attaching arguments correctly. Notice that the 378 noun arguments constitute 65% of the total 586 noun attachments, while the 104 verb arguments amount to only 35% of the 294 verb attachments. Furthermore, performance with verb adjuncts is worse than with noun adjuncts. Thus much of the problem with V attachments noted in the previous section appears to be attributable to a problem with adjuncts, particularly verbal ones. Performance on verbal arguments remains worse than performance on nominal ones, however.</Paragraph>
      <Paragraph position="2"> The remaining cases are all complex in some way, and the performance is poor on these classes, showing clearly the need for a more elaborated model of the syntactic structure that is being identified.</Paragraph>
      <Paragraph position="3"> 5. Comparison with a Dictionary The idea that lexical preference is a key factor in resolving structural ambiguity leads us naturally to ask whether existing dictionaries can provide information relevant to disambiguation. The Collins COBUILD English Language Dictionary (Sinclair et al.</Paragraph>
      <Paragraph position="4"> 1987) is useful for a comparison with the AP sample for several reasons: it was compiled on the basis of a large text corpus, and thus may be less subject to idiosyncrasy  than other works, and it provides, in a separate field, a direct indication of prepositions typically associated with many nouns and verbs. From a machine-readable version of the dictionary, we extracted a list of 1,942 nouns associated with a particular preposition, and of 2,291 verbs associated with a particular preposition after an object noun phrase. 8 These 4,233 pairs are many fewer than the number of associations in the AP sample (see Table 6), even if we ignore the most infrequent pairs. Of the total 76,597 pairs, 20,005 have a frequency greater than 3, and 7,822 have a frequency that is greater than 3 and more than 4 times what one would predict on the basis of the unigram frequencies of the noun or verb and the preposition. 9 We can use the fixed lexicon of noun-preposition and verb-preposition associations derived from COBUILD to choose attachment in our test set. The COBUILD dictionary has information on 257 of the 880 test verb-noun-preposition triples. In 241 of those cases, there is information only on noun or only on verb association. In these cases, we can use the dictionary to choose the attachment according to the association indicated. In the remaining 16 cases, associations between the preposition and both the noun and the verb are recorded in the dictionary. For these, we select noun attachment, since it is the more probable outcome in general. For the remaining cases, we assume that the dictionary makes no decision. Table 7 gives the results obtained</Paragraph>
      <Paragraph position="6"> where U is ~w,J(w, p), the total number of token bigrams. It is equivalent to w and p having a mutual information (defined as I(w, p) = log 2 f@ f_~u ) greater than 2. This threshold of 2, of course, is an arbitrary cutoff.  by this attachment procedure. The precision figure is similar to that obtained by the lexical association procedure with a threshold of zero, but the recall is far lower: the dictionary provides insufficient information in most cases.</Paragraph>
      <Paragraph position="7"> Like the lexicon derived from the COBUILD dictionary, the fixed lexicon of 7,822 corpus-derived associations derived from our bigram table as described above (that is, all bigrams where f(w~ p) &gt; 3 and I(w, p) &gt; 2) contains categorical information about associations. Using it for disambiguation in the way the COBUILD dictionary was used gives the results indicated in Table 7. The precision is similar to that which was achieved with the LA procedure with a threshold of 2, although the recall is lower. This suggests that while overall coverage of association pairs is important, the information about the relative strengths of associations contributing to the LA score is also significant.</Paragraph>
      <Paragraph position="8"> It must be noted that the dictionary information we derived from COBUILD was composed for people to use in printed form. It seems likely that associations were left out because they did not serve this purpose in one way or another. For instance, listing many infrequent or semantically predictable associations might be confusing. Furthermore, our procedure undoubtedly gained advantage from the fact that the test items are drawn from the same body of text as the training corpus. Nevertheless, the results of this comparison suggest that for the purpose of this paper, a partially parsed corpus is a better source of information than a dictionary. This conclusion should not be overstated, however. Table 6 showed that most of the associations in each lexicon are not found in the others. Table 8 is a sample of a verb-preposition association dictionary obtained by merging information from the AP sample and from COBUILD, illustrating both the common ground and the differences between the two lexicons.</Paragraph>
      <Paragraph position="9"> Each source of information provides intuitively important associations that are missing from the other.</Paragraph>
    </Section>
  </Section>
  <Section position="6" start_page="117" end_page="118" type="metho">
    <SectionTitle>
6. Discussion
</SectionTitle>
    <Paragraph position="0"> In our judgment, the results of the lexical association procedure are good enough to make it useful for some purposes, in particular for inclusion in a parser such as Fidditch. The fact that the LA score provides a measure of confidence increases this usefulness, since in some applications (such as exploratory linguistic analysis of text  Donald Hindle and Mats Rooth Structural Ambiguity and Lexical Relations Table 8 Verb--(NP)-Preposition associations in the COBUILD dictionary and in the AP sample (with f(v~ p) &gt; 3 and I(v~ p) &gt; 2.0).</Paragraph>
  </Section>
  <Section position="7" start_page="118" end_page="119" type="metho">
    <SectionTitle>
AP sample COBUILD
</SectionTitle>
    <Paragraph position="0"> approach about as at with appropriate for for approve because by for under  corpora) it is advantageous to be able to achieve increased precision in exchange for discarding a proportion of the data. From another perspective, our results are less good than what might be demanded. The performance of the human judges with access just to the verb-noun-preposition triple is a standard of what is possible based on this information, and the lexical association procedure falls somewhat short of this standard. The analysis of underlying relations indicated some particular areas in which the procedure did not do well, and where there is therefore room for improvemenf. In particular, performance on adjuncts was poor. A number of classes of adjuncts, such as temporal ones, are fairly easy to identify once information about the object of the preposition is taken into account. Beginning with such an identification step (which could be conceived of as adding a feature such as \[+temporal\] to individual prepositions, or replacing individual token prepositions with an abstract temporal preposition) might yield a lexical association procedure that would do better with adjuncts. But it is also possible that a procedure that evaluates associations with individual nouns and verbs is simply inappropriate for adjuncts. This is an area for further investigation.</Paragraph>
    <Paragraph position="1"> This experiment was deliberately limited to one kind of attachment ambiguity. However, we expect that the method will be extendable to other instances of PP attachment ambiguity, such as the ambiguity that arises when several prepositional phrases follow a subject NP, and to ambiguities involving other phrases, especially phrases such as infinitives that have syntactic markers analogous to a preposition.  Computational Linguistics Volume 19, Number 1 We began this paper by alluding to several approaches to PP attachment, specifically work assuming the construction of discourse models, approaches based on structural attachment preferences, and work indicating a dominant role for lexical preference. Our results tend to confirm the importance of lexical preference. However, we can draw no firm conclusions about the other approaches. Since our method yielded incorrect results on roughly 20% of the cases, its coverage is far from complete. This leaves a lot of work to be done, within both psycholinguistic and computational approaches. Furthermore, as we noted above, contemporary psycholinguistic work is concerned with modeling the time course of parsing. Our experiment gives no information about how lexical preference information is exploited at this level of detail, or the importance of such information compared with other factors such as structural .preferences at a given temporal stage of the human parsing process. However, the numerical estimates of lexical association we have obtained may be relevant to a psycholinguistic investigation of this issue.</Paragraph>
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