| { |
| "paper_id": "P99-1015", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T09:31:48.085176Z" |
| }, |
| "title": "Corpus-Based Linguistic Indicators for Aspectual Classification", |
| "authors": [ |
| { |
| "first": "Eric", |
| "middle": [ |
| "V" |
| ], |
| "last": "Siegel", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Columbia University New York", |
| "location": { |
| "postCode": "10027", |
| "region": "NY" |
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| }, |
| "email": "" |
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| "abstract": "Fourteen indicators that measure the frequency of lexico-syntactic phenomena linguistically related to aspectual class are applied to aspectual classification. This group of indicators is shown to improve classification performance for two aspectual distinctions, stativity and completedness (i.e., telicity), over unrestricted sets of verbs from two corpora. Several of these indicators have not previously been discovered to correlate with aspect.", |
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| "abstract": [ |
| { |
| "text": "Fourteen indicators that measure the frequency of lexico-syntactic phenomena linguistically related to aspectual class are applied to aspectual classification. This group of indicators is shown to improve classification performance for two aspectual distinctions, stativity and completedness (i.e., telicity), over unrestricted sets of verbs from two corpora. Several of these indicators have not previously been discovered to correlate with aspect.", |
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| "body_text": [ |
| { |
| "text": "Aspectual classification maps clauses to a small set of primitive categories in order to reason about time.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "For example, events such as, \"You called your father,\" are distinguished from states such as, \"You resemble your father.\" These two high-level categories correspond to primitive distinctions in many domains, e.g., the distinction between procedure and diagnosis in the medical domain.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "Aspectual classification further distinguishes events according to completedness (i.e., telicity), which determines whether an event reaches a culmination point in time at which a new state is introduced. For example, \"I made a fire\" is culminated, since a new state is introduced -something is made, whereas, \"I gazed at the sunset\" is non-culminated.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "Aspectual classification is necessary for interpreting temporal modifiers and assessing temporal entailments (Vendler, 1967; Dowty, 1979; Moens and Steedman, 1988; Dorr, 1992) , and is therefore a necessary component for applications that perform certain natural language interpretation, natural language generation, summarization, information retrieval, and machine translation tasks.", |
| "cite_spans": [ |
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| "start": 109, |
| "end": 124, |
| "text": "(Vendler, 1967;", |
| "ref_id": "BIBREF25" |
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| { |
| "start": 125, |
| "end": 137, |
| "text": "Dowty, 1979;", |
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| "end": 163, |
| "text": "Moens and Steedman, 1988;", |
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| "text": "Dorr, 1992)", |
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| "section": "Introduction", |
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| "text": "Aspect introduces a large-scale, domaindependent lexical classification problem. Although an aspectual lexicon of verbs would suffice to classify many clauses by their main verb only, a verb's primary class is often domaindependent (Siegel, 1998b) . Therefore, it is necessary to produce a specialized lexicon for each domain.", |
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| "start": 232, |
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| "text": "(Siegel, 1998b)", |
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| "section": "Introduction", |
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| "text": "Most approaches to automatically categorizing words measure co-occurrences between open-class lexical items (Schfitze, 1992; Hatzivassiloglou and McKeown, 1993; Pereira et al., 1993) . This approach is limited since cooccurrences between open-class lexical items is sparse, and is not specialized for particular semantic distinctions such as aspect.", |
| "cite_spans": [ |
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| "start": 108, |
| "end": 124, |
| "text": "(Schfitze, 1992;", |
| "ref_id": "BIBREF19" |
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| "start": 125, |
| "end": 160, |
| "text": "Hatzivassiloglou and McKeown, 1993;", |
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| "start": 161, |
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| "text": "Pereira et al., 1993)", |
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| "section": "Introduction", |
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| "text": "In this paper, we describe an expandable framework to classify verbs with linguisticallyspecialized numerical indicators. Each linguistic indicator measures the frequency of a lexicosyntactic marker, e.g. the perfect tense. These markers are linguistically related to aspect, so the indicators are specialized for aspectual classification in particular. We perform an evaluation of fourteen linguistic indicators over unrestricted sets of verbs from two corpora. When used in combination, this group of indicators is shown to improve classification performance for two aspectual distinctions: stativity and completedness. Moreover, our analysis reveals a predictive value for several indicators that have not previously been discovered to correlate with aspect in the linguistics literature.", |
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| "text": "The following section further describes aspect, and introduces linguistic insights that are exploited by linguistic indicators. The next section describes the set of linguistic indicators evaluated in this paper. Then, our experimental method and results are given, followed by a discussion and conclusions. (Moens and Steedman, 1988 Table 1 summarizes the three aspectual distinctions, which compose five aspectual categories. In addition to the two distinctions described in the previous section, atomicity distinguishes events according to whether they have a time duration (punctual versus extended). Therefore, four classes of events are derived: culmination, culminated process, process, and point. These aspectual distinctions are defined formally by Dowty (1979) . Several researchers have developed models that incorporate aspectual class to assess temporal constraints between clauses (Passonneau, 1988; Dorr, 1992) . For example, stativity must be identified to detect temporal constraints between clauses connected with when, e.g., in interpreting (1),", |
| "cite_spans": [ |
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| "start": 308, |
| "end": 333, |
| "text": "(Moens and Steedman, 1988", |
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| "start": 758, |
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| "text": "Dowty (1979)", |
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| "text": "(Passonneau, 1988;", |
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| "text": "Dorr, 1992)", |
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| "text": "Table 1", |
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| "text": "(1) She had good strength when objectively tested. (2) Phototherapy was discontinued when the bilirubin came down to 13. the temporal relationship is different:", |
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| { |
| "text": "COme I I discontinue I I", |
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| "text": "These aspectual distinctions are motivated by a series of entailment constraints. In particular, certain lexico-syntactic features of a clause, such as temporal adjuncts and tense, are constrained by and contribute to the aspectual class of the clause (Vendler, 1967; Dowty, 1979) . Tables 2 illustrates an array of linguistic con- As a second example, an event must be culminated to appear in the perfect tense, for example,", |
| "cite_spans": [ |
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| "start": 252, |
| "end": 267, |
| "text": "(Vendler, 1967;", |
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| { |
| "start": 268, |
| "end": 280, |
| "text": "Dowty, 1979)", |
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| "sec_num": "1" |
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| { |
| "text": "She had made an attempt. (culm.), which contrasts with, (7) *He has cowered down. (non-culm.)", |
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| "text": "The best way to exploit aspectual markers is not obvious, since, while the presence of a marker in a particular clause indicates a constraint on the aspectual class of the clause, the absence thereof does not place any constraint. Therefore, as with most statistical methods for natural language, the linguistic constraints associated with markers are best exploited by a system that measures co-occurrence frequencies. For example, a verb that appears more frequently in the progressive is more likely to describe an event. Klavans and Chodorow (1992) pioneered the application of statistical corpus analysis to aspectuai classification by ranking verbs according to the frequencies with which they occur with certain aspectual markers. tor has a unique value for each verb. The first indicator, frequency, is simply the frequency with which each verb occurs over the entire corpus. The remaining 13 indicators measure how frequently each verb occurs in a clause with the named linguistic marker. For example, the next three indicators listed measure the frequency with which verbs 1) are modified by not or never, 2) are modified by a temporal adverb such as then or frequently, and 3) have no deep subject (e.g., passive phrases such as, \"She was admitted to the hospital\"). Further details regarding these indicators and their linguistic motivation is given by Siegel (1998b) .", |
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| "start": 525, |
| "end": 552, |
| "text": "Klavans and Chodorow (1992)", |
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| }, |
| { |
| "start": 1365, |
| "end": 1379, |
| "text": "Siegel (1998b)", |
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| "eq_spans": [], |
| "section": "Linguistic Indicators", |
| "sec_num": "3" |
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| "text": "There are several reasons to expect superior classification performance when employing multiple linguistic indicators in combination rather than using them individually. While individual indicators have predictive value, they are predictively incomplete. This incompleteness has been illustrated empirically by showing that some indicators help for only a subset of verbs (Siegel, 1998b) . Such incompleteness is due in \u2022 part to sparsity and noise of data when computing indicator values over a corpus with limited size and some parsing errors. However, this incompleteness is also a consequence of the linguistic characteristics of various indicators. For example:", |
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| "start": 372, |
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| "text": "\u2022 Aspectual coercion such as iteration compromises indicator measurements (Moens and Steedman, 1988) . For example, a punctual event appears with the progressive in, \"She was sneezing for a week.\" (point --, process --. culminated process) In this example, for a week can only modify an extended event, requiring the first coercion. In addition, this for-PP also makes an event culminated, causing the second transformation.", |
| "cite_spans": [ |
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| "start": 74, |
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| "sec_num": "3" |
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| "text": "\u2022 Some aspectual markers such as the pseudo-cleft and manner adverbs test for intentional events, and therefore are not compatible with all events, e.g., \"*I died diligently.\"", |
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| "text": "\u2022 The progressive indicator's predictiveness for stativity is compromised by the fact that many location verbs can appear with the progressive, even in their stative sense, e.g. \"The book was lying on the shelf.\" (Dowty, 1979) \u2022 Several indicators measure phenomena that are not linguistically constrained by any aspectuM category, e.g., the present tense, frequency and not/never indicators.", |
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| "start": 213, |
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| "text": "(Dowty, 1979)", |
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| "text": "In this section, we evaluate the set of fourteen linguistic indicators for two aspectual distinctions: stativity and completedness. Evaluation is over corpora of medical reports and novels, respectively. This data is summarized in Table 4 (available at www. CS. columbia, edu/~evs/YerbData).", |
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| "text": "Table 4 (available at", |
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| "text": "First, linguistic indicators are each evaluated individually. A training set is used to select indicator value thresholds for classification. Then, we report the classification performance achieved by combining multiple indicators. In this case, the training set is used to optimize a model for combining indicators. In both cases, evaluation is performed over a separate test set of clauses.", |
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| "text": "The combination of indicators is performed by four standard supervised learning algorithms: decision tree induction (Quinlan, 1986) , CART (Friedman, 1977) , log-linear regression (Santner and Duffy, 1989) and genetic programming (GP) (Cramer, 1985; Koza, 1992) .", |
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| "text": "A pilot study showed no further improvement in accuracy or recall tradeoff by additional learning algorithms: Naive Bayes ( Hart, 1973) , Ripper (Cohen, 1995) , ID3 (Quinlan, 1986), C4.5 (Quinlan, 1993) , and metalearning to combine learning methods (Chan and Stolfo, 1993) .", |
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| "text": "Our experiments are performed across a corpus of 3,224 medical discharge summaries. A medical discharge summary describes the symptoms, history, diagnosis, treatment and outcome of a patient's visit to the hospital. These reports were parsed with the English Slot Grammar (ESG) (McCord, 1990) , resulting in 97,973 clauses that were parsed fully with no selfdiagnostic errors (ESG produced error messages on 12,877 of this corpus' 51,079 complex sentences).", |
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| "start": 278, |
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| "text": "(McCord, 1990)", |
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| "section": "Stativity", |
| "sec_num": "4.1" |
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| "text": "Be and have, the two most popular verbs, covering 31.9% of the clauses in this corpus, are handled separately from all other verbs. Clauses with be as their main verb, comprising 23.9% of the corpus, always denote a state. Clauses with have as their main verb, composing 8.0% of the corpus, are highly ambiguous, and have been addressed separately by considering the direct object of such clauses (Siegel, 1998a) . 4.1.1 Manual Marking 1,851 clauses from the parsed corpus were manually marked according to stativity. As a linguistic test for marking, each clause was tested for readability with \"What happened was... ,1 A comparison between human markers for this test performed over a different corpus is reported below in Section 4.2.1. Of these, 373", |
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| "text": "1 Manual labeling followed a strict set of linguisticallymotivated guidelines, e.g., negations were ignored (Siegel, 199Sb Table 5 : Indicators discriminate between states and events.", |
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| "text": "clauses were rejected because of parsing problems. This left 1,478 clauses, divided equally into training and testing sets. 83.8% of clauses with main verbs other than be and have are events, which thus provides a baseline method of 83.8% for comparison. Since our approach examines only the main verb of a clause, classification accuracy over the test cases has a maximum of 97.4% due to the presence of verbs with multiple classes.", |
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| "section": "Stativity", |
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| "text": "The values of the indicators listed in Table 5 were computed, for each verb, across the 97,973 parsed clauses from our corpus of medical discharge summaries.", |
| "cite_spans": [], |
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| "section": "Individual Indicators", |
| "sec_num": "4.1.2" |
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| "text": "The second and third columns of Table 5 show the average value for each indicator over stative and event clauses, as measured over the training examples. For example, 4.44% of stative clauses are modified by either not or never, but only 1.56% of event clauses were so modified.", |
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| "text": "The fourth column shows the results of Ttests that compare indicator values over stative training cases to those over event cases for each indicator. As shown, the differences in stative and event means are statistically significant (p < .01) for the first seven indicators.", |
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| "text": "Each indicator was tested individually for classification accuracy by establishing a classification threshold over the training data, and validating performance over the testing data using the same threshold. Only the frequency indicator succeeded in significantly improving clas- ", |
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| "text": "Three machine learning methods successfully combined indicator values, improving classification accuracy over the baseline measure. As shown in Table 6 , the decision tree attained the highest accuracy, 93.9%. Binomial tests showed this to be a significant improvement over the 88.0% accuracy achieved by the frequency indicator alone, as well as over the other two learning methods. No further improvement in classification performance was achieved by CART. The increase in the number of stative clauses correctly classified, i.e. stative recall, illustrates an even greater improvement over the baseline. As shown in Table 6 , the three learning methods achieved stative recalls of 74.2%, 47.4% and 34.2%, as compared to the 0.0% stative recall achieved by the baseline, while only a small loss in recall over event clauses was suffered. The baseline does not classify any stative clauses correctly because it classifies all clauses as events.", |
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| "text": "Table 6", |
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| "section": "Indicators in Combination", |
| "sec_num": "4.1.3" |
| }, |
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| "text": "Classification performance is equally competitive without the frequency indicator, although this indicator appears to dominate over others. When decision tree induction was employed to combine only the 13 indicators other than frequency, the resulting decision tree achieved 92.4% accuracy and 77.5% stative recall.", |
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| "section": "Indicators in Combination", |
| "sec_num": "4.1.3" |
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| "text": "In medical discharge summaries, nonculminated event clauses are rare. Therefore, our experiments for classification according to completedness are performed across a corpus of ten novels comprising 846,913 words. These novels were parsed with ESG, resulting in 75,289 fully-parsed clauses (22,505 of 59,816 sentences produced errors).", |
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| "section": "Completedness", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "884 clauses from the parsed corpus were manually marked according to completedness. Of these, 109 were rejected because of parsing problems, and 160 rejected because they described states. The remaining 615 clauses were divided into training and test sets such that the distribution of classes was equal. The baseline method in this case achieves 63.3% accuracy.", |
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| "section": "Manual Marking", |
| "sec_num": "4.2.1" |
| }, |
| { |
| "text": "The linguistic test was selected for this task by Passonneau (1988) : If a clause in the past progressive necessarily entails the past tense reading, the clause describes a non-culminated event. For example, We were talking just like men (non-culm.) entails that We talked just like men, but The woman was building a house (culm.) does not necessarily entail that The woman built a house. Cross-checking between linguists shows high agreement. In particular, in a pilot study manually annotating 89 clauses from this corpus according to stativity, two linguists agreed 81 times. Of 57 clauses agreed to be events, 46 had agreement with respect to completedness.", |
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| "start": 50, |
| "end": 67, |
| "text": "Passonneau (1988)", |
| "ref_id": "BIBREF14" |
| } |
| ], |
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| "eq_spans": [], |
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| "text": "The verb say (point), which occurs nine times in the test set, was initially marked incorrectly as culminated, since points are non-extended and therefore cannot be placed in the progressive. After some initial experimentation, we corrected the class of each occurrence of say in the data. Table 7 is analogous to Table 5 for completeness. The differences in culminated and nonculminated means are statistically significant (p < .05) for the first six indicators. However, for completedness, no indicator was shown to significantly improve classification accuracy over the baseline. Table 8 : Comparison of four learning methods and two performance baselines, distinguishing culminated from non-culminated events.", |
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| "start": 290, |
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| "text": "Table 7", |
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| "start": 583, |
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| }, |
| { |
| "text": "As shown in Table 8 , the highest accuracy, 74.0%, was attained by CART. A binomial test shows this is a significant improvement over the 63.3% baseline.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 12, |
| "end": 19, |
| "text": "Table 8", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Indicators in Combination", |
| "sec_num": "4.2.3" |
| }, |
| { |
| "text": "The increase in non-culminated recall illustrates a greater improvement over the baseline. As shown in Table 8 , non-culminated recalls of up to 53.6% were achieved by the learning methods, compared to 0.0%, achieved by the baseline.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 103, |
| "end": 110, |
| "text": "Table 8", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Indicators in Combination", |
| "sec_num": "4.2.3" |
| }, |
| { |
| "text": "Additionally, a non-culminated F-measure of 61.9 was achieved by GP, when optimizing for F-Measure, improving over 53.7 attained by the optimal uninformed baseline. F-measure computes a tradeoff between recall and precision (Van Rijsbergen, 1979) . In this work, we weigh recall and precision equally, in which case, recall*precision F -measure = (recall+precision)f2 Automatic methods highly prioritized the perfect indicator. The induced decision tree uses the perfect indicator as its first discriminator, log-linear regression ranked the perfect indicator as fourth out of fourteen, function trees created by GP include the perfect indicator as one of five indicators used together to increase classification performance, and the perfect indicator tied as most highly correlated with completedness (cf. Table 7) .", |
| "cite_spans": [ |
| { |
| "start": 224, |
| "end": 246, |
| "text": "(Van Rijsbergen, 1979)", |
| "ref_id": "BIBREF24" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 807, |
| "end": 815, |
| "text": "Table 7)", |
| "ref_id": "TABREF11" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Indicators in Combination", |
| "sec_num": "4.2.3" |
| }, |
| { |
| "text": "Since certain verbs are aspectually ambiguous, and, in this work, clauses are classified by their main verb only, a second baseline approach would be to simply memorize the majority aspect of each verb in the training set, and classify verbs in the test set accordingly. In this case, test verbs that did not appear in the training set would be classified according to majority class. However, classifying verbs and clauses according to numerical indicators has several important advantages over this baseline:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discussion", |
| "sec_num": "5" |
| }, |
| { |
| "text": "\u2022 Handles rare or unlabeled verbs. The results we have shown serve to estimate classification performance over \"unseen\" verbs that were not included in the supervised training sample. Once the system has been trained to distinguish by indicator values, it can automatically classify any verb that appears in unlabeled corpora, since measuring linguistic indicators for a verb is fully automatic. This also applies to verbs that are underrepresented in the training set. For example, one node of the resulting decision tree trained to distinguish according to stativity identifies 19 stative test cases without misclassifying any of 27 event test cases with verbs that occur only one time each in the training set. \u2022 Success when training doesn't include test verbs. To test this, all test verbs were eliminated from the training set, and log-linear regression was trained over this smaller set to distinguish according to completedness. The result is shown in Table 8 (\"llr2\"). Accuracy remained higher than the baseline \"br' (bl2 not applicable), and the recall tradeoff is felicitous. . Improved performance.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 960, |
| "end": 967, |
| "text": "Table 8", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Discussion", |
| "sec_num": "5" |
| }, |
| { |
| "text": "Memorizing majority aspect does not achieve as high an accuracy as the linguistic indicators for completedness, nor does it achieve as wide a recall tradeff for both stativity and completedness. These results are indicated as the second baselines (\"bl2\") in tables 6 and 8, respectively.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discussion", |
| "sec_num": "5" |
| }, |
| { |
| "text": "\u2022 Scalar values assigned to each verb allow the tradeoff between recall and precision to be selected for particular applications by selecting the classification threshold. For example, in a separate study, optimizing for F-measure resulted in a more dramatic tradeoff in recall values as compared to those attained when optimizing for accuracy (Siegel, 1998b) . Moreover, such scalar values can provide input to systems that perform reasoning on fuzzy or uncertainty knowledge. \u2022 This framework is expandable since additional indicators can be introduced by measuring the frequencies of additional aspectual markers. Furthermore, indicators measured over multiple clausal constituents, e.g., main verb-object pairs, alleviate verb ambiguity and sparsity and improve classification performance (Siegel, 1998b) .", |
| "cite_spans": [ |
| { |
| "start": 344, |
| "end": 359, |
| "text": "(Siegel, 1998b)", |
| "ref_id": "BIBREF23" |
| }, |
| { |
| "start": 793, |
| "end": 808, |
| "text": "(Siegel, 1998b)", |
| "ref_id": "BIBREF23" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Discussion", |
| "sec_num": "5" |
| }, |
| { |
| "text": "We have developed a full-scale system for aspectual classification with multiple linguistic indicators. Once trained, this system can automatically classify all verbs appearing in a corpus, including \"unseen\" verbs that were not included in the supervised training sample. This framework is expandable, since additional lexicosyntactic markers may also correlate with aspectual class. Future work will extend this approach to other semantic distinctions in natural language. Linguistic indicators successfully exploit linguistic insights to provide a much-needed method for aspectual classification. When combined with a decision tree to classify according to stativity, the indicators achieve an accuracy of 93.9% and stative recall of 74.2%. When combined with CART to classify according to completedness, indicators achieved 74.0% accuracy and 53.1% non-culminated recall.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "A favorable tradeoff in recall presents an advantage for applications that weigh the identification of non-dominant classes more heavily (Cardie and Howe, 1997) . For example, correctly identifying occurrences of for that denote event durations relies on positively identifying non-culminated events. A system that summarizes the duration of events which incorrectly classifies \"She ran (for a minute)\" as culminated will not detect that \"for a minute\" describes the duration of the run event. This is because durative for-PPs that modify culminated events denote the duration of the ensuing state, e.g., I leJt the room for a minute. (Vendler, 1967) Our analysis has revealed several insights regarding individual indicators. For example, both duration in-PP and manner adverb are particularly valuable for multiple aspectual distinctions -they were ranked in the top two positions by log-linear modeling for both stativity and completedness.", |
| "cite_spans": [ |
| { |
| "start": 137, |
| "end": 160, |
| "text": "(Cardie and Howe, 1997)", |
| "ref_id": "BIBREF0" |
| }, |
| { |
| "start": 635, |
| "end": 650, |
| "text": "(Vendler, 1967)", |
| "ref_id": "BIBREF25" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "We have discovered several new linguistic indicators that are not traditionally linked to aspectual class. In particular, verb frequency with no deep subject was positively correlated with both stativity and completedness. Moreover, four other indicators are newly linked to stativity: (1) Verb frequency, (2) occurrences modified by \"not\" or \"never\", (3) occurrences in the past or present participle, and (4) occurrences in the perfect tense. Additionally, another three were newly linked to completedness: (1) occurrences modified by a manner adverb, (2) occurrences in the past or present participle, and (3) occurrences in the progressive.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "These new correlations can be understood in pragmatic terms. For example, since points (non-culminated, punctual events, e.g., hiccup) are rare, punctual events are likely to be culminated. Therefore, an indicator that discriminates events according to extendedness, e.g., the progressive, past/present participle, and duration for-PP, is likely to also discriminate between culminated and non-culminated events.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "As a second example, the not/never indicator correlates with stativity in medical reports because diagnoses (i.e., states) are often ruled out in medical discharge summaries, e.g., \"The patient was not hypertensive,\" but procedures (i.e., events) that were not done are not usually mentioned, e.g., '~.An examination was not performed.\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Conclusions", |
| "sec_num": "6" |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "Kathleen R. McKeown was extremely helpful regarding the formulation of this work and Judith L. Klavans regarding linguistic techniques, and they, along with Min-Yen Kan and Dragomir R. Radev provided useful feedback on an earlier draft of this paper.This research was supported in part by the Columbia University Center for Advanced Technology in High Performance Computing and Communications in Healthcare (funded by the New York State Science and Technology Foundation), the Office of Naval Research under contract N00014-95-1-0745 and by the National Science Foundation under contract GER-90-24069.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgements", |
| "sec_num": null |
| } |
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| "TABREF0": { |
| "html": null, |
| "type_str": "table", |
| "num": null, |
| "text": "Aspectual classes. This table comes from Moens and Steedman", |
| "content": "<table/>" |
| }, |
| "TABREF2": { |
| "html": null, |
| "type_str": "table", |
| "num": null, |
| "text": "", |
| "content": "<table><tr><td colspan=\"2\">: Several aspectual markers and associated</td></tr><tr><td colspan=\"2\">constraints on aspectual class, primarily from Kla-</td></tr><tr><td>vans' summary (1994).</td><td/></tr><tr><td colspan=\"2\">If a clause can occur: then it is:</td></tr><tr><td>with a temporal adverb</td><td>Event</td></tr><tr><td>(e.g., then)</td><td/></tr><tr><td>in progressive</td><td>Extended</td></tr><tr><td/><td>Event</td></tr><tr><td>with a duration in-PP</td><td>Culm Event</td></tr><tr><td>(e.g., in an hour)</td><td/></tr><tr><td>in the perfect tense</td><td>Culm Event</td></tr><tr><td/><td>or State</td></tr><tr><td colspan=\"2\">straints. Each entry in this table describes an</td></tr><tr><td colspan=\"2\">aspectual marker and the constraints on the as-</td></tr><tr><td colspan=\"2\">pectual category of any clause that appears with</td></tr><tr><td colspan=\"2\">that marker. For example, a clause must be</td></tr><tr><td colspan=\"2\">an extended event to appear in the progressive</td></tr><tr><td>tense, e.g.,</td><td/></tr><tr><td colspan=\"2\">(3) He was prospering in India. (extended),</td></tr><tr><td>which contrasts with,</td><td/></tr><tr><td colspan=\"2\">(4) *You were noticing it. (punctual).</td></tr><tr><td>and,</td><td/></tr><tr><td colspan=\"2\">(5) *She was seeming sad. (state).</td></tr></table>" |
| }, |
| "TABREF3": { |
| "html": null, |
| "type_str": "table", |
| "num": null, |
| "text": "", |
| "content": "<table><tr><td>lists the linguistic indicators evalu-</td></tr><tr><td>ated for aspectual classification. Each indica-</td></tr></table>" |
| }, |
| "TABREF4": { |
| "html": null, |
| "type_str": "table", |
| "num": null, |
| "text": "Fourteen linguistic indicators evaluated for aspectual classification.", |
| "content": "<table/>" |
| }, |
| "TABREF6": { |
| "html": null, |
| "type_str": "table", |
| "num": null, |
| "text": "Two classification problems on different data sets.", |
| "content": "<table/>" |
| }, |
| "TABREF9": { |
| "html": null, |
| "type_str": "table", |
| "num": null, |
| "text": "", |
| "content": "<table><tr><td>: Comparison of three learning methods</td></tr><tr><td>and two performance baselines, distinguishing states</td></tr><tr><td>from events.</td></tr><tr><td>sification accuracy by itself, achieving an accu-</td></tr><tr><td>racy of 88.0%. This improvement in accuracy</td></tr><tr><td>was achieved simply by discriminating the pop-</td></tr><tr><td>ular verb show as a state~ but classifying all</td></tr><tr><td>other verbs as events. Although many domains</td></tr><tr><td>may primarily use show as an event, its appear-</td></tr><tr><td>ances in medical discharge summaries, such as,</td></tr></table>" |
| }, |
| "TABREF11": { |
| "html": null, |
| "type_str": "table", |
| "num": null, |
| "text": "Indicators discriminate between culminated and non-culminated events.", |
| "content": "<table><tr><td/><td/><td colspan=\"2\">Culminated</td><td>Non-Culm</td></tr><tr><td/><td>acc</td><td>recall</td><td colspan=\"2\">prec recall</td><td>prec</td></tr><tr><td colspan=\"5\">CART 74.0% 86.2% 76.0% 53.1% 69.0%</td></tr><tr><td>llr</td><td colspan=\"4\">70.5% 83.1% 73.6% 48.7% 62.5%</td></tr><tr><td>lit2</td><td colspan=\"4\">67.2% 81.5% 71.0% 42.5% 57.1%</td></tr><tr><td>GP</td><td colspan=\"4\">68.6% 77.3% 74.2% 53.6% 57.8%</td></tr><tr><td>dt</td><td colspan=\"4\">68.5% 86.2% 70.6% 38.1% 61.4%</td></tr><tr><td>bl</td><td colspan=\"4\">63.3% 100.0% 63.3% 0.0% 100.0%</td></tr><tr><td>b12</td><td colspan=\"4\">70.8% 94.9% 69.8% 29.2% 76.7%</td></tr></table>" |
| } |
| } |
| } |
| } |
