Datasets:

WINGNUS
/

ACL-OCL

	{
	"paper_id": "J93-2002",
	"header": {
	"generated_with": "S2ORC 1.0.0",
	"date_generated": "2023-01-19T02:59:09.277169Z"
	},
	"title": "From Grammar to Lexicon: Unsupervised Learning of Lexical Syntax",
	"authors": [
	{
	"first": "Michael",
	"middle": [
	"R"
	],
	"last": "Brent",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "Johns Hopkins University",
	"location": {}
	},
	"email": "michael@mail.cog.jhu.edu"
	}
	],
	"year": "",
	"venue": null,
	"identifiers": {},
	"abstract": "Imagine a language that is completely unfamiliar; the only means of studying it are an ordinary grammar book and a very large corpus of text. No dictionary is available. How can easily recognized, surface grammatical facts be used to extract from a corpus as much syntactic information as possible about individual words? This paper describes an approach based on two principles. First, rely on local morpho-syntactic cues to structure rather than trying to parse entire sentences. Second, treat these cues as probabilistic rather than absolute indicators of syntactic structure. Apply inferential statistics to the data collected using the cues, rather than drawing a categorical conclusion from a single occurrence of a cue. The effectiveness of this approach for inferring the syntactic frames of verbs is supported by experiments on an English corpus using a program called Lerner.",
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	"abstract": [
	{
	"text": "Imagine a language that is completely unfamiliar; the only means of studying it are an ordinary grammar book and a very large corpus of text. No dictionary is available. How can easily recognized, surface grammatical facts be used to extract from a corpus as much syntactic information as possible about individual words? This paper describes an approach based on two principles. First, rely on local morpho-syntactic cues to structure rather than trying to parse entire sentences. Second, treat these cues as probabilistic rather than absolute indicators of syntactic structure. Apply inferential statistics to the data collected using the cues, rather than drawing a categorical conclusion from a single occurrence of a cue. The effectiveness of this approach for inferring the syntactic frames of verbs is supported by experiments on an English corpus using a program called Lerner.",
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	"section": "Abstract",
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	"text": "This paper presents a study in the automatic acquisition of lexical syntax from naturally occurring English text. It focuses on discovering the kinds of syntactic phrases that can be used to represent the semantic arguments of particular verbs. For example, want can take an infinitive argument and hope a tensed clause argument, but not vice versa:",
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	"section": "Introduction",
	"sec_num": "1."
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	{
	"text": "(1) a. b. c. d.",
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	"section": "Introduction",
	"sec_num": "1."
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	{
	"text": "John wants Mary to be happy. John hopes that Mary is happy. John wants that Mary is happy. John hopes Mary to be happy.",
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	"section": "Introduction",
	"sec_num": "1."
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	"text": "This study focuses on the ability of verbs to take arguments represented by infinitives, tensed clauses, and noun phrases serving as both direct and indirect objects. These lexical properties are similar to those that Chomsky (1965) termed subcategorization frames, but to avoid confusion the properties under study here will be referred to as syntactic frames or simply frames. The general framework for the problems addressed in this paper can be thought of as follows. Imagine a language that is completely unfamiliar; the only means of studying it are an ordinary grammar book and a very large corpus of text (or transcribed speech) . No dictionary is available. How can easily recognized, surface grammatical The effectiveness of this approach for inferring the syntactic frames of verbs is supported by experiments using an implementation called Lerner. In the spirit of the problem stated above, Lerner starts out with no knowledge of content words--it bootstraps from determiners, auxiliaries, modals, prepositions, pronouns, complementizers, coordinating conjunctions, and punctuation. Lerner has two independent components corresponding to the two strategies listed above. The first component identities sentences where a particular verb is likely to be exhibiting a particular syntactic frame. It does this using local cues, such as the that the cue. This component keeps track of the number of times each verb appears with cues for each syntactic frame as well as the total number of times each verb occurs. This process can be described as collecting observations and its output as an observations table. A segment of an actual observations table is shown in Table 4 . The observations table serves as input to the statistical modeler, which ultimately decides whether the accumulated evidence that a particular verb manifests a particular syntactic frame in the input is reliable enough to warrant a conclusion.",
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	"start": 218,
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	"text": "Chomsky (1965)",
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	"text": "(or transcribed speech)",
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	"section": "Introduction",
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	"text": "To the best of my knowledge, this is the first attempt to design a system that autonomously learns syntactic frames from naturally occurring text. The goal of learning syntactic frames and the learning framework described above lead to three major differences between the approach reported here and most recent work in learning grammar from text. First, this approach leverages a little a priori grammatical knowledge using statistical inference. Most work on corpora of naturally occurring language either uses no a priori grammatical knowledge (Brill and Marcus 1992; Ellison 1991; Finch and Chater 1992; Pereira and Schabes 1992) , or else it relies on a large and complex grammar (Hindle 1990 (Hindle , 1991 . One exception is Magerman and Marcus (1991) , in which a small grammar is used to aid learning. 1 A second difference is that the work reported here uses inferential rather than descriptive statistics. In other words, it uses statistical methods to infer facts about the language as it exists in the minds of those who produced the corpus. Many other projects have used statistics in a way that summarizes facts about the text but does not draw any explicit conclusions from them (Finch and Chater 1992; Hindle 1990 ). On the other hand, Hindle (1991) does use inferential statistics, and Brill (1992) recognizes the value of inference, although he does not use inferential statistics per se. Finally, many other projects in machine learning of natural language use input that is annotated in some way, either with part-of-speech tags (Brill 1992; Brill and Marcus 1992; Magerman and Marcus 1990) or with syntactic brackets (Pereira and Schabes 1992) .",
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	"text": "Magerman and Marcus 1990)",
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	"section": "Introduction",
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	"text": "The remainder of the paper is organized as follows. Section 2 describes the morphosyntactic cues Lerner uses to collect observations. Section 3 presents the main contribution of this paper--the statistical model and experiments supporting its effectiveness. Finally, Section 4 draws conclusions and lays out a research program in machine learning of natural language.",
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	"section": "Introduction",
	"sec_num": "1."
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	"text": "This section describes the local morpho-syntactic cues that Lerner uses to identify likely examples of particular syntactic frames. These cues must address two problems: finding verbs in the input and identifying phrases that represent arguments to the verb. The next two subsections present cues for these tasks. The cues presented here are not intended to be the last word on local cues to structure in English; they are merely intended to illustrate the feasibility of such cues and demonstrate how the statistical model accommodates their probabilistic correspondence to the true syntactic structure of sentences. Variants of these cues are presented in Brent (1991a Brent ( , 1991b . The final subsection summarizes the procedure for collecting observations and discusses a sample of the observations table collected from the Brown corpus.",
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	"text": "Brent (1991a",
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	"text": "Brent ( , 1991b",
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	"section": "Collecting Observations",
	"sec_num": "2."
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	"text": "Lerner identifies verbs in two stages, each carried out on a separate pass through the corpus. First, strings that sometimes occur as verbs are identified. Second, occurrences of those strings in context are judged as likely or unlikely to be verbal occurrences. The second stage is necessary because of lexical ambiguity.",
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	"section": "Finding Verbs",
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	"text": "The first stage uses the fact that all English verbs can occur both with and without the suffix -ing. Words are taken as potential verbs if and only if they display this alternation in the corpus. 2 There are a few words that meet this criterion but do not occur as verbs, including income~incoming (,incame/incomed), ear~earring, her~herring, and middle~middling. However, the second stage of verb detection, combined with the statistical criteria, prevent these pairs from introducing errors.",
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	"section": "Finding Verbs",
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	"text": "1 Brill and Marcus (1992) use a single grammatical rule in the test phase to supplement the rules their system learns, but no grammatical knowledge is used in the learning phase. 2 Morphological analyzers typically use a root lexicon to resolve the ambiguities in morphological adjustment rules (Karttunen 1983) . The system described here uses rules similar to those of Karttunen and Wittenburg (1983) , but it resolves the ambiguities using only the contents of the corpus. This technique will be described in a subsequent paper.",
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	"text": "Brill and Marcus (1992)",
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	"text": "(Karttunen 1983)",
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	"text": "Karttunen and Wittenburg (1983)",
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	"section": "Finding Verbs",
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	"text": "Lerner assumes that a potential verb is functioning as a verb unless the context suggests otherwise. In particular, an occurrence of a potential verb is taken as a nonverbal occurrence only if it follows a determiner or a preposition other than to. For example, was talking would be taken as a verb, but a talk would not. This precaution reduces the likelihood that a singular count noun will be mistaken for a verb, since singular count nouns are frequently preceded by a determiner.",
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	"text": "Finally, the only morphological forms that are used for learning syntactic frames are the stem form and the -ing form. There are several reasons for this. First, forms ending in -s are potentially ambiguous between third person singular present verbs and plural nouns. Since plural nouns are not necessarily preceded by determiners (I like to take walks), they could pose a significant ambiguity problem. Second, past participles do not generally take direct objects: knows me and knew me are OK, but not \u2022 is known me. Further, the past tense and past participle forms of some verbs are identical, while those of others are distinct. As a result, using the -ed forms would have complicated the statistical model substantially. Since the availability of raw text is not generally a limiting factor, it makes sense to wait for the simpler cases.",
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	"text": "When a putative occurrence of a verb is found, the next step is to identify the syntactic types of nearby phrases and determine whether or not they are likely to be arguments of the verb.",
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	"section": "Identifying Argument Phrases",
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	"text": "First, assume that a phrase P and a verb V have been identified in some sentence. Lerner's strategy for determining whether P is an argument to V has two components: .",
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	"section": "Identifying Argument Phrases",
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	"text": "If P is a noun phrase (NP), take it as an argument only if there is evidence that it is not the subject of another clause.",
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	"text": "Regardless of P's category, take it as an argument only if it occurs to the right of V and there are no potential attachment points for P between V and P.",
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	"text": "For example, suppose that the sequence that the were identified as the left boundary of a clause in the sentence I want to tell him that the idea won'tfly. Because pronouns like him almost never take relative clauses, and because pronouns are known at the outset, Lerner concludes that the clause beginning with that the is probably an argument of the verb tell. 3 It is always possible that it could be an argument of the previous verb want, but Lerner treats that as unlikely. On the other hand, if the sentence were I want to tell the boss that the idea won'tfly, then Lerner cannot determine whether the clause beginning with that the is an argument to tell or is instead related to boss, as in I want to fire the boss that the workers don't trust.",
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	"text": "Now consider specific cues for identifying argument phrases. The phrase types for which data are reported here are noun phrases, infinitive verb phrases (VPs), and tensed clauses. These phrase types yield three syntactic frames with a single argument and three with two arguments, as shown in Table 1 . The cues used for identifying these frames are shown in Tables 2 and 3. Table 2 defines lexical categories that are referred to in Table 3 . The category V in Table 3 starts out empty and is filled as verbs are detected on the first pass. \"cap\" stands for any capitalized word and \"cap+\" for any sequence of capitalized words. These cues are applied by matching them against the string of words immediately to the right of each verb. For example, a verb V is [ our [ their [ your [ this [ that [ whose +TNS: has [ have[ had [ am [ is [ are [ was [ were [ do [ does [ did [ can [ could [ may [ might [ must [ would CC: when [ before [ after [ as [ while [ if PUNC: [ ? [ ! [ , [ ; [ : will [ Table 3 Cues for syntactic frames. The category V is initially empty and is filled out during the first pass. \"cap\" stands for any capitalized word and \"cap+\" stands for any sequence of capitalized words. recorded as having occurred with a direct object and no other argument phrase if V is followed by a pronoun of ambiguous case and then a coordinating conjunction, as in I'll see you when you return from Mexico. The coordinating conjunction makes it unlikely that the pronoun is the subject of another clause, as in I see you like champagne.",
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	"text": "[ our [ their [ your [ this [ that [ whose +TNS: has [ have[ had [ am [ is [ are [ was [ were [ do [ does [ did [ can [ could [ may [ might [ must [ would CC: when [ before [ after [ as [ while [ if PUNC: [ ? [ ! [ , [ ; [ : will",
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	"text": "It also makes it unlikely that the verb has an additional NP argument, as in I'II tell you my secret recipe.",
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	"text": "To summarize, the procedure for collecting observations from a corpus is as follows:",
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	"section": "Summary and Sample Data",
	"sec_num": "2.3"
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	"text": "1. Go through the corpus once finding pairs of words such that one is the result of adding the suffix -ing to the other, applying appropriate morphological adjustment rules. List members of such pairs as verbs.",
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	"text": "2. Go through the corpus again. At each word w that is on the list of verbs, (a) If w is not preceded by a preposition or a determiner, increment the number of times that w appears as a verb. (b) If any of the cues listed in Table 3 match the words immediately following w, increment the number of times that w appears to occur in the corresponding frame.",
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	"text": "Table 3 match the words immediately",
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	"text": "3. Combine the data for the stem form and the -ing form. Table 4 shows an alphabetically contiguous portion of the observations table that results from applying this procedure to the Brown Corpus (untagged). Each row represents data collected from one pair of words, including both the -ing form and the stem form. The first column, titled V, represents the total number of times the word occurs in positions where it could be functioning as a verb. Each subsequent column represents a single frame. The number appearing in each row and column represents the number of times that the row's verb cooccurred with cues for the column's frame. Zeros are omitted. Thus recall and recalling occurred a combined total of 42 times, excluding those occurrences that followed determiners or prepositions. Three of those occurrences were followed by a cue for a single NP argument and four were followed by cues for a tensed clause argument.",
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	"text": "Table 4",
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	"text": "Judgments based on the observations in Table 4 , made by the method of Section 3.",
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	"section": "Table 5",
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	"text": "NP, cl recognize NP, cl recover NP refuse inf",
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	"section": "recall",
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	"text": "The cues are fairly rare, so verbs in Table 4 that occur fewer than 15 times tend not to occur with these cues at all. Further, these cues occur fairly often in structures other than those they are designed to detect. For example, record, recover, and refer all occurred with cues for an infinitive, although none of them in fact takes an infinitive argument. The sentences responsible for these erroneous observations are:",
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	},
	{
	"text": "(2) (a) (b) (c) (d)",
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	"text": "But I shall campaign on the Meyner record to meet the needs of the years ahead. Sposato needed a front, some labor stiff with a clean record to act as business agent of the Redhook local. Then last season the Birds tumbled as low as 11-18 on May 19 before recovering to make a race of it and total 86 victories. But I suspect that the old Roman was referring to change made under military occupation--the sort of change which Tacitus was talking about when ....",
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	"text": "In (2a,b) record occurs as a noun. In (2c) recover is a verb but the infinitive VP, to make a race of it .... does not appear to be an argument. In any case, it does not bear the same relation to the verb as the infinitive arguments of verbs like try, want, hope, ask, and refuse. In (2d) refer is a verb but to change is a PP rather than an infinitive.",
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	"text": "The remainder of this paper describes and evaluates a method for making judgments about the ability of verbs to appear in particular syntactic frames on the basis of noisy data like that of Table 4. Given the data in Table 4 , that method yields the judgments in Table 5 .",
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	"text": "Table 4",
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	"text": "Table 5",
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	{
	"text": "As noted above, the correspondence between syntactic structure and the cues that Lerner uses is not perfect. Mismatches between cue and structure are problematic because naturally occurring language provides no negative evidence. If a V verb is followed by a cue for some syntactic frame S, that provides evidence that V does occur in frame S, but there is no analogous source of evidence that V does not occur in frame S.",
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	"section": "Statistical Modeling",
	"sec_num": "3."
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	"text": "The occurrence of mismatches between cue and structure can be thought of as a random process where each occurrence of a verb V has some non-zero probability of being followed by a cue for a frame S, even if V cannot in fact occur in S. If this model is accurate, the more times V occurs, the more likely it is to occur at least once with a cue for S. The intransitive verb arrive, for example, will eventually occur with a cue for an NP argument, if enough text is considered. A learner that considers a single occurrence of verb followed by a cue to be conclusive evidence will eventually come to the false conclusion that arrive is transitive. In other words, the information provided by the cues will eventually be washed out by the noise. This problem is inherent in learning from naturally occurring language, since infallible parsing is not possible. The only way to prevent it is to consider the frequency with which each verb occurs with cues for each frame. In other words, to consider each occurrence of V without a cue for S as a small bit of evidence against V being able to occur in frame S. This section describes a statistical technique for weighing such evidence.",
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	"section": "Statistical Modeling",
	"sec_num": "3."
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	"text": "Given a syntactic frame S, the statistical model treats each verb V as analogous to a biased coin and each occurrence of V as analogous to a flip of that coin. An occurrence that is followed by a cue for S corresponds to one outcome of the coin flip, say heads; an occurrence without a cue for S corresponds to tails. 4 If the cues were perfect predictors of syntactic structure then a verb V that does not in fact occur in frame S would never appear with cues for S--the coin would never come up heads. Since the cues are not perfect, such verbs do occur with cues for S. The problem is to determine when a verb occurs with cues for S often enough that all those occurrences are unlikely to be errors.",
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	"section": "Statistical Modeling",
	"sec_num": "3."
	},
	{
	"text": "In the following discussion, a verb that in fact occurs in frame S in the input is described as a +S verb; one that does not is described as a -S verb. The statistical model is based on the following approximation: for fixed S, all -S verbs have equal probability of being followed by a cue for S. Let ~r-s stand for that probability. ~r-s may vary from frame to frame, but not from verb to verb. Thus, errors might be more common for tensed clauses than for NPs, but the working hypothesis is that all intransitives, such as saunter and arrive, are about equally likely to be followed by a cue for an NP argument. If the error probability ~r-s were known, then we could use the standard hypothesis testing method for binomial frequency data. For example, suppose 7r-s = .05--on average, one in twenty occurrences of a -S verb is followed by a cue for S. If some verb V occurs 200 times in the corpus, and 20 of those occurrences are followed by cues for S, that ought to suggest that V is unlikely to have probability .05 of being followed by a cue for S, and hence V is unlikely to be -S. Specifically, the chance of flipping 20 or more heads out of 200 tosses of a coin with a five percent chance of coming up heads each time is less than three in 1000. On the other hand, it is not all that unusual to flip 2 or more heads out of 20 on such a coin--it happens about one time in four. If a verb occurs 20 times in the corpus and 2 of those occurrences are followed by cues for S, it is quite possible that V is -S and that the 2 occurrences with cues for S are explained by the five percent error rate on -S verbs.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Statistical Modeling",
	"sec_num": "3."
	},
	{
	"text": "The next section reviews the hypothesis-testing method and gives the formulas for computing the probabilities of various outcomes of coin tosses, given the coin's bias. It also provides empirical evidence that, for some values of 7r_s, hypothesis-testing does a good job of distinguishing +S verbs from -S verbs that occur with cues for S because of mismatches between cue and structure. The following section proposes a method for estimating ~r-s and provides empirical evidence that its estimates are nearly optimal.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Statistical Modeling",
	"sec_num": "3."
	},
	{
	"text": "The statistical component of Lerner is designed to prevent the information provided by the cues from being washed out by the noise. The basic approach is hypothesis testing on binomial frequency data (Kalbfleisch 1985) . Specifically, a verb V is shown to be +S by assuming that it is -S and then showing that if this were true, the observed pattern of cooccurrence of V with cues for S would be extremely unlikely.",
	"cite_spans": [
	{
	"start": 200,
	"end": 218,
	"text": "(Kalbfleisch 1985)",
	"ref_id": "BIBREF12"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "3.1.1 Binomial Frequency Data. In order to use the hypothesis testing method we need to estimate the probability ~r-s that an occurrence of a verb V will be followed by a cue for S if V is -S. In this section it is assumed that 7r_s is known. The next section suggests a means of estimating Tr_s. In both sections it is also assumed that for each +S verb, V, the probability that V will be followed by a cue for S is greater than 7r_s. Other than that, no assumptions are made about the probability that a +S verb will be followed by a cue for S. For example, two verbs with transitive senses, such as cut and walk, may have quite different frequencies of cooccurrence with cues for NP.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "It does not matter what these frequencies are as long as they are greater than lr_Np.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "If a coin has probability p of flipping heads, and if it is flipped n times, the probability of its coming up heads exactly m times is given by the binomial distribution:",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "n~ P(m,n,p) -m!(n -m)! pm(1 -p)n-m (1)",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "The probability of coming up heads m or more times is given by the obvious sum:",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "n P(m+, n, p) = ~ P(i, n, p) (2) i=m",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "Analogously, P (m+, n, ~r-s) gives the probability that m or more occurrences of a -S verb V will be followed by a cue for S out of n occurrences total.",
	"cite_spans": [
	{
	"start": 15,
	"end": 28,
	"text": "(m+, n, ~r-s)",
	"ref_id": null
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "If m out of n occurrences of V are followed by cues for S, and if P (m+, n, ~r-s) is quite small, then it is unlikely that V is -S. Traditionally, a threshold less than or equal to .05 is set such that a hypothesis is rejected if, assuming the hypothesis were true, the probability of outcomes as extreme as the observed outcome would be below the threshold. The confidence attached to this conclusion increases as the threshold decreases.",
	"cite_spans": [
	{
	"start": 68,
	"end": 81,
	"text": "(m+, n, ~r-s)",
	"ref_id": null
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "3.1.2 Experiment. The experiment presented in this section is aimed at determining how well the method presented above can distinguish +S verbs from -S verbs. Let p-s be an estimate of 7r_s. It is conceivable that P(m+,n,p-s) might not be a good predictor of whether or not a verb is +S, regardless of the estimate p-s. For example, if the correspondence between the cues and the structures they are designed to detect were quite weak, then many -S verbs might have lower P(m+,n,p-s) than many +S verbs. This experiment measures the accuracy of binomial hypothesis testing on the data collected by Lerner's cues as a function of p-s. In addition to showing that P(m+, n, P-s) is good for distinguishing +S and -S verbs, these data provide a baseline against which to compare methods for estimating the error rate 7r_s.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "Method The cues described in Section 2 were applied to the Brown Corpus (untagged version). Equation 2 was applied to the resulting data with a cutoff of P(m+,n,p_s) < .02 and p-s varying between 2 -5 (1 error in every 32 occurrences) and 2 -13 (1 error in every 8192 occurrences). The resulting judgments were compared to the blind judgments of a single judge. One hundred ninety-three distinct verbs were chosen at random from the tagged version of the Brown Corpus for comparison. Common verbs are more likely to be included in the test sample than rare verbs, but Comparison of automatic classification to hand judgments for tensed-clause complement as a function of estimated error rate p (Brown Corpus). PRE = (TP / TP + FP); REC = (TP / TP + FN). no verb is included more than once. Each verb was scored for a given frame only if it cooccurs with a cue for that frame at least once. Thus, although 193 verbs were randomly selected from the corpus for scoring, only the 63 that cooccur with a cue for tensed clause at least once were scored for the tensed-clause frame. This procedure makes it possible to evaluate the hypothesis-testing method on data collected by the cues, rather than evaluating the cues per se. It also makes the judgment task much easier--it is not necessary to determine whether a verb can appear in a frame in principle, only whether it does so in particular sentences. There were, however, five cases where the judgments were unclear. These five were not scored. See Appendix C for details.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Hypothesis Testing",
	"sec_num": "3.1"
	},
	{
	"text": "The results of these comparisons are summarized in Table 6 (tensed clause) and Table 7 ( Discussion For verbs taking just a tensed clause argument, Table 6 shows that, given the right estimate P-s of lr_s, it is possible to classify these 63 verbs with only 1 false positive and 8 false negatives. If the error rate were ignored or approximated as zero then the false positives would go up to 19. On the other hand, if the error rate were taken to be as high as 1 in 25 then the false negatives would go up to 20. In this case, the sum of both error types is minimized with 2 -8 < P-c1 _< 2 -1\u00b0. Table 7 shows similar results for verbs taking just an infinitive argument, where misclassifications are minimized with p-inf = 2-7. ",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 51,
	"end": 74,
	"text": "Table 6 (tensed clause)",
	"ref_id": "TABREF4"
	},
	{
	"start": 79,
	"end": 88,
	"text": "Table 7 (",
	"ref_id": "TABREF6"
	},
	{
	"start": 148,
	"end": 155,
	"text": "Table 6",
	"ref_id": "TABREF4"
	},
	{
	"start": 596,
	"end": 603,
	"text": "Table 7",
	"ref_id": "TABREF6"
	}
	],
	"eq_spans": [],
	"section": "Results",
	"sec_num": null
	},
	{
	"text": "As before, assume that an occurrence of a -S verb is followed by a cue for S with probability 7r_s. Also as before, assume that for each +S verb V, the probability that an occurrence of V is followed by a cue for S is greater than 7r_s. It is useful to think of the verbs in the corpus as analogous to a large bag of coins with various biases, or probabilities of coming up heads. The only assumption about the distribution of biases is that there is some definite but unknown minimum bias 7r_s. 5 Determining whether or not a verb appears in frame S is analogous to determining, for some randomly selected coin, whether its bias is greater than ~r-s. The only available evidence comes from selecting a number of coins at random and flipping them. The previous section showed how this can be done given an estimate of ~r-s.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "Suppose a series of coins is drawn at random from the bag. Each coin is flipped N times. It is then assigned to a histogram bin representing the number of times it came up heads. At the end of this sampling procedure bin i contains the number of coins that came up heads exactly i times out of N. Such a histogram is shown in Figure 1 , where N = 40. If N is large enough and enough coins are flipped N times, one would expect the following:",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 326,
	"end": 334,
	"text": "Figure 1",
	"ref_id": null
	}
	],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "1. The coins whose probability of turning up heads is ~r-s (the minimum) should cluster at the low-heads end of the histogram. That is, there should be some 0 __G j0 _< N such that most of the coins that turn up j0 heads or fewer have probability 7r_s, and, conversely, most coins with probability ~r-s turn up j0 heads or fewer.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "2. Suppose j0 were known. Then the portion of the histogram below j0 should have a roughly binomial shape. In Figure 1 , for example, the first eight bins have roughly the shape one would expect if j0 were 8. In contrast, the first 16 bins do not have the shape one would expect if j0",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 110,
	"end": 118,
	"text": "Figure 1",
	"ref_id": null
	}
	],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "5 If the number of coins is taken to be infinite, then the biases must be not only greater than ~r-s but bounded above ~r-s. A histogram illustrating a binomially shaped distribution in the first eight bins.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": ". were 16---their height drops to zero for two stretches before rising significantly above zero again. Specifically, the height of the i th histogram bin should be roughly proportional to P(i, N, P-s) , with N the fixed sample size and P-s an estimate of 7r_s.",
	"cite_spans": [
	{
	"start": 188,
	"end": 200,
	"text": "P(i, N, P-s)",
	"ref_id": null
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "Suppose again that j0 were known. Then the average rate at which the coins in bins j0 or lower flip heads is a good estimate of ~r-s.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "The estimation procedure tries out each bin as a possible estimate of j0. Each estimate of j0 leads to an estimate of ~r-s and hence to an expected shape for the first j0 histogram bins. Each estimate j of j0 is evaluated by comparing the predicted distribution in the first j bins to the observed distribution--the better the fit, the better the estimate.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "Moving from coins to verbs, the procedure works as follows. For some fixed N, consider the first N occurrences of each verb that occurs at least N times in the input. (A uniform sample size N is needed only for estimating 7r-s. Given an estimate of 7r-s, verbs with any number of occurrences can be classified.) Let S be some syntactic frame and let H[i] be the number of distinct verbs that were followed by cues for S exactly i times out of N--i.e., the height of the ith histogram bin. Assume that there is some 1 ~ j0 _< N such that most -S verbs are followed by cues for S j0 times or fewer, and conversely most verbs that are followed by cues for S j0 times or fewer are -S verbs. For each possible estimate j of j0 there is a corresponding estimate of 7r_s; namely, the average rate at which verbs in the first j bins are followed by cues for S. Choosing the most plausible estimate of 7r_s thus comes down to choosing the most plausible estimate of j0, the boundary between the -S verbs and the rest of the histogram. To evaluate the plausibility of each possible estimate j of j0, measure the fit between the predicted distribution of -S verbs, assuming j is the boundary of the -S cluster, and the observed distribution of the -S verbs, also assuming j is the boundary of the -S cluster. Given j, let p-s stand for the average rate at which verbs in bins j or lower are followed by cues for S. The predicted distribution for -S verbs is proportional to P(i,N,p-s) for 0 < i < N. The observed distribution of -S verbs, assuming j is the boundary of the -S cluster, is H[i] for 0 < i < j and 0 for j < i < N. Measure the fit between the predicted and observed distributions by normalizing both to have unit area and taking the sum over 0 < i < N of the squares of the differences between the two distributions at each bin i. In pseudo-code, the procedure is as follows: Choose the p-s yielding the best~it if sum-of-squares < min-sum-of-squares then rain-sum-of-squares := sum-of-squares best-estimate := P-s return best-estimate 3.2.1 Experiment. This section evaluates the proposed estimation technique empirically in terms of the errors it yields when the cues of Section 2 are applied to the Brown Corpus. The sample selection and scoring procedures are the same as in the previous section. When ~r-s is estimated using sample size N --100, Table 8 shows the results for each of the six frames. Varying N between 50 and 150 results in no significant change in the estimated error rates. One way to judge the value of the estimation and hypothesis-testing methods is to examine the false positives. Three of the five false positives result from errors in verb detection that are not distributed uniformly across verbs. In particular, shock, board, and near are used more often as nonverbs than as verbs. This creates many opportunities for nonverbal occurrences of these words to be mistaken for verbal occurrences. Other verbs, like know, are unambiguous and thus are not subject to this type of error. As a result, these errors violate the model's assumption that errors are distributed uniformly across verbs and highlight the limitations of the model. The remaining false positives were touch and belong, both mistaken as taking an NP followed by a tensed clause. The touch error was caused by the capitalization of the first word of a line of poetry:",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 2353,
	"end": 2360,
	"text": "Table 8",
	"ref_id": "TABREF7"
	}
	],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "ESTIMATE",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "I knew not what did to a friend belong Till I stood up, true friend, by thy true side;",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "Till was mistaken for a proper name. The belong error was caused by mistaking a matrix clause for an argument in:",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "With the blue flesh of night touching him he stood under a gentle hill caressing the flageolet with his lips, making it whisper.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "It seems likely that such input would be much rarer in more mundane sources of text, such as newspapers of record, than in the diverse Brown Corpus.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "The results for infinitives and clauses can also be judged by comparison to the optimal classifications rates from Tables 6 and 7. In both cases the classification appears to be right in the optimal range. In fact, the estimated error rate for infinitives produces a better classification than any of those shown in Table 7 . (It falls at a value between those shown.) The classification of clauses and infinitives remains in the optimal range when the probability threshold is varied from .01 to .05.",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 316,
	"end": 323,
	"text": "Table 7",
	"ref_id": "TABREF6"
	}
	],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "Overall the tradeoff between improved precision and reduced recall seems quite good, as compared to doing no noise reduction (P-s = 0). The only possible exception is the NP frame, where noise reduction causes 59 false negatives in exchange for preventing only 5 false positives. This is partly explained by the different prior probabilities of the different frames. Most verbs can take a direct object argument, whereas most verbs cannot take a direct object argument followed by a tensed clause argument. There is no way to know this in advance. There may be other factors as well. If the error rate for the NP cues is substantially lower than 1 out of 100, then it cannot be estimated accurately with sample size N = 100. On the other hand, if the sample size N is increased substantially there may not be enough verbs that occur N times or more in the corpus. So a larger corpus might improve the recall rate for NP.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Estimating the Error Rate",
	"sec_num": "3.2"
	},
	{
	"text": "This paper explores the possibility of using simple grammatical regularities to learn lexical syntax. The data presented in Tables 6, 7 , and 8 provide evidence that it is possible to learn significant aspects of English lexical syntax in this way. Specifically, these data suggest that neither a large parser nor a large lexicon is needed to recover enough syntactic structure for learning lexical syntax. Rather, it seems that significant lexical syntactic information can be recovered using a few approximate cues along with statistical inference based on a simple model of the cues' error distributions.",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 124,
	"end": 135,
	"text": "Tables 6, 7",
	"ref_id": "TABREF4"
	}
	],
	"eq_spans": [],
	"section": "General Discussion",
	"sec_num": "4."
	},
	{
	"text": "The lexical entry of a verb can specify other syntactic frames in addition to the six studied here. In particular, many verbs take prepositional phrases (PPs) headed by a particular preposition or class of prepositions. For example, put requires a location as a second argument, and locations are often represented by PPs headed by locative prepositions. Extending Lerner to detect PPs is trivial. Since the set of prepositions in the language is essentially fixed, all prepositions can be included in the initial lexicon. Detecting a PP requires nothing more than detecting a preposition. 6 The statistical model can, of course, be applied without modification.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Other Syntactic Frames",
	"sec_num": "4.1"
	},
	{
	"text": "The problem, however, is determining which PPs are arguments and which are adjuncts. There are clearly cases where a prepositional phrase can occur in a clause not by virtue of the lexical entry of the verb but rather by virtue of nonlexical facts of English syntax. For instance, almost any verb can occur with a temporal PP headed by on, as in John arrived on Monday. Such PPs are called adjuncts. On the other hand, the sense of on in John sprayed water on the ceiling is quite different. This sense, it can be argued, is available only because the lexical entry of spray specifies a location argument that can be realized as a PP. If anything significant is to be learned about individual words, the nonspecific cooccurrences of verbs with PPs (adjuncts) must be separated from the specific ones (arguments). It is not clear how a machine learning system could do this, although frequency might provide some clue. Worse, however, there are many cases in which even trained linguists lack clear intuitions. Despite a number of attempts to formulate necessary and sufficient conditions for the argument/adjunct distinction (e.g., Jackendoff 1977) , there are many cases for which the various criteria do not agree or the judgments are unclear (Adams and Macfarland 1991) . Thus, the Penn Treebank does not make the argument/adjunct distinction because their judges do not agree often enough. Until a useful definition that trained humans can agree on is developed, it would seem fruitless to attempt machine learning experiments in this domain.",
	"cite_spans": [
	{
	"start": 1132,
	"end": 1148,
	"text": "Jackendoff 1977)",
	"ref_id": "BIBREF11"
	},
	{
	"start": 1245,
	"end": 1272,
	"text": "(Adams and Macfarland 1991)",
	"ref_id": "BIBREF0"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Other Syntactic Frames",
	"sec_num": "4.1"
	},
	{
	"text": "Although the results of this study are generally encouraging, they also point to some limitations of the statistical model presented here. First, it does not take into account variation in the percentage of verbs that can appear in each frame. For example, most verbs can take an NP argument, while very few can take an NP followed by a tensed clause. This results in too few verbs being classified as +NP and too many being classified as +NPcl, as shown in Table 8 . Second, it does not take into account the fact that for some words with verbal senses most of their occurrences are verbal, whereas for others most of their occurrences are nonverbal. For example, operate occurs exclusively as a verb while board occurs much more often as a noun than as a verb. Since the cues are based on the assumption that the word in question is a verb, board presents many more opportunities for error than operate. This violates the assumption that the probability of error for a given frame is approximately uniform across verbs. 6 The preposition/particle distinction is set aside here in order to focus on the more problematic argument/adjunct distinction. These limitations do not constitute a major impediment to applications of the current results. For example, an applied system can be provided with the rough estimates that 80-95 percent of verbs take a direct object, while 1-2 percent take a direct object followed by a tensed clause. Such estimates can be expected to reduce misclassification significantly. Further, an existing dictionary could be used to \"train\" a statistical model on familiar verbs. A trained system would probably be more accurate in classifying new verbs. Finally, the lexical ambiguity problem could probably be reduced substantially in the applied context by using a statistical tagging program (Brill 1992; Church 1988) .",
	"cite_spans": [
	{
	"start": 1822,
	"end": 1834,
	"text": "(Brill 1992;",
	"ref_id": "BIBREF3"
	},
	{
	"start": 1835,
	"end": 1847,
	"text": "Church 1988)",
	"ref_id": "BIBREF6"
	}
	],
	"ref_spans": [
	{
	"start": 458,
	"end": 465,
	"text": "Table 8",
	"ref_id": "TABREF7"
	}
	],
	"eq_spans": [],
	"section": "Limitations of the Statistical Model",
	"sec_num": "4.2"
	},
	{
	"text": "For addressing basic questions in machine learning of natural language the solutions outlined above are not attractive. All of those solutions provide the learner with additional specific knowledge of English, whereas the goal for the machine learning effort should be to replace specific knowledge with general knowledge about the types of regularities to be found in natural language.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Limitations of the Statistical Model",
	"sec_num": "4.2"
	},
	{
	"text": "There is one approach to the lexical ambiguity problem that does not require giving the learner additional specific knowledge. The problem is as follows: words that occur frequently as, say, nouns are likely to have a different error rate from unambiguous verbs. If it were known which words occur primarily as verbs and which occur primarily as nouns then separate error rate estimates could be made for each. This would reduce the rate of false positive errors even without any further information about which particular occurrences are nominal and which are verbal. One way to distinguish primarily nominal words from primarily verbal words is by the relative frequencies of their various inflected forms. For example, Table 9 shows the contrast in the distribution of inflected forms between project and board on the one hand and operate and follow on the other. Project and board are two words whose frequent occurrence as nouns has caused Lerner to make false positive errors. In both cases, the stem and -s forms are much more common than the -ed and -ing forms. Compare this to the distribution for the unambiguous verbs operate and follow. In these cases the diversity of frequencies is much lower and does not display the characteristic pattern of a word that occurs primarily as a noun---ing and -ed forms that are much rarer than the -s and stem forms. Similar characteristic patterns exist for words that occur primarily as adjectives. Recognizing such ambiguity patterns automatically would allow a separate error rate to be estimated for the highly ambiguous words.",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 722,
	"end": 729,
	"text": "Table 9",
	"ref_id": "TABREF8"
	}
	],
	"eq_spans": [],
	"section": "Limitations of the Statistical Model",
	"sec_num": "4.2"
	},
	{
	"text": "From the perspective of computational language acquisition, a natural direction in which to extend this work is to develop algorithms for learning some of the specific knowledge that was programmed into the system described above. Consider the mor-phological adjustment rules according to which, for example, the final \"e\" of bite is deleted when the suffix -ing is added, yielding biting rather than ,\"biteing.\" Lerner needs to know such rules in order to determine whether or not a given word occurs both with and without the suffix -ing. Experiments are under way on an unsupervised procedure that learns such rules from English text, given only the list of English verbal suffixes. This work is being extended further in the direction of discovering the morphemic suffixes themselves and discovering the ways in which these suffixes alternate in paradigms. The short-term goal is to develop algorithms that can learn the rules of inflection in English starting from only a corpus and a general notion of the nature of morphological regularities.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Future Work",
	"sec_num": "4.3"
	},
	{
	"text": "Ultimately, this line of inquiry may lead to algorithms that can learn much of the grammar of a language starting with only a corpus and a general theory of the kinds of formal regularities to be found in natural languages. Some elements of syntax may not be learnable in this way (Lightfoot 1991) , but the lexicon, morphology, and phonology together make up a substantial portion of the grammar of a language. If it does not prove possible to learn these aspects of grammar starting from a general ontology of linguistic regularities and using distributional analysis then that, too, is an interesting result. It would suggest that the task requires a more substantive initial theory of possible grammars, or some semantic information about input sentences, or both. In any case this line of inquiry promises to shed light on the nature of language, learning, and language learning.",
	"cite_spans": [
	{
	"start": 281,
	"end": 297,
	"text": "(Lightfoot 1991)",
	"ref_id": "BIBREF15"
	}
	],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Future Work",
	"sec_num": "4.3"
	},
	{
	"text": "Thanks to Don Hindle for this observation (personal communication).",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "",
	"sec_num": null
	},
	{
	"text": "Given a verb V, the outcomes of the coins for different S's are treated as approximately independent, even though they cannot be perfectly independent. Their dependence could be modeled using a multinomial rather than a binomial model, but the experimental data suggest that this is unnecessary.",
	"cite_spans": [],
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	"section": "",
	"sec_num": null
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	],
	"back_matter": [
	{
	"text": "Of the 193 verbs listed above, Lerner detects 174 in the untagged version of the Brown Corpus. Of these 174, there are 87 for which Lerner does not find sufficient evidence to prove that they have any of the six syntactic frames in question. Some of these genuinely do not appear in the corpus with cues for any of the six, while others do appear with cues, but not often enough to provide reliable evidence. Given more text, sufficient evidence might eventually accumulate for many of these verbs.The 87 that were detected but not assigned any frames are as follows: The 87 verbs for which Lerner does find sufficient evidence to assign one or more frames are shown in Table 10 . Reading across each row, a verb is assigned those frames",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 670,
	"end": 678,
	"text": "Table 10",
	"ref_id": null
	}
	],
	"eq_spans": [],
	"section": "Appendix B: Complete Output",
	"sec_num": null
	},
	{
	"text": "The lexicon that Lerner produces when restricted to the 193 test verbs. whose symbols appear in its row. For easy reference by frame, all the symbols for a given frame are aligned in one column.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "Table 10",
	"sec_num": null
	},
	{
	"text": "The results provided in Tables 6, 7 , and 8 are based on hand judgments of the examples found by the cues. In most cases these judgments were clear, but there were five difficult judgments. These five, which were not scored, are discussed below. In all cases except the last Lerner did not find sufficient evidence to warrant a conclusion.Provide with a tensed clause Provided and providing, when they occur without auxiliaries, clearly take a tensed clause, as in (3a).. a.",
	"cite_spans": [],
	"ref_spans": [
	{
	"start": 24,
	"end": 35,
	"text": "Tables 6, 7",
	"ref_id": null
	}
	],
	"eq_spans": [],
	"section": "Appendix C: Difficult Judgments",
	"sec_num": null
	},
	{
	"text": "Squat-style lifters and leg-split lifters would both benefit enormously by practicing those variations providing that they remember to make alternate sets with the left and right leg to the front. There must be a restriction in the deed to provide that the customer may not be charged more than the current market price for the oil, Provided and providing do not appear to be functioning as verbs when they take a tensed clause. Tensed forms of provide do not take a tensed clause, as in ,\"She {provided, provides} that he have enough to live on.\" However, the infinitive form of provide in (3b) is clearly functioning as a verb. The best generalization of these observations is unclear.Act with an infinitive (4) \"E. B.\" compared John Brown to Moses in that they were both acting to deliver millions from oppression.In 4, it is not clear whether the infinitive is an argument, as it would be in \"they were both trying to deliver ...,\" or a purpose adjunct, as in \"they were both breaking the law to deliver .... \" Live with an infinitive Live occurred twice in the expression live to see and once in the expression live to hear.If this expression were completely fixed, then its properties should not be considered properties of the verb live. However, the infinitive can be any perception verb and possibly a few others. The expression is limited, but not frozen, so the appropriate conclusion is unclear.Work with a single NP Work takes a direct object in the sense meaning form or mold. The cues do not detect this sense of work in the corpus, but they do find it followed by adverbial NPs such as \"every day\" and in the expression work their will.Give with a single NP Lerner judged give to take a single NP based on seven examples of which six were mistaken. The seventh was the sentence \"They continued to give an arm-elevation.\" This is ungrammatical in my dialect, but it is clearly an example of give with a single NP complement.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "b.",
	"sec_num": null
	}
	],
	"bib_entries": {
	"BIBREF0": {
	"ref_id": "b0",
	"title": "Testing for adjuncts",
	"authors": [
	{
	"first": "L",
	"middle": [],
	"last": "Adams",
	"suffix": ""
	},
	{
	"first": "T",
	"middle": [],
	"last": "Macfarland",
	"suffix": ""
	}
	],
	"year": 1991,
	"venue": "Proceedings, Second Annual Meeting of the Formal Linguistics Society of Midamerica. Formal Linguistics Society of Midamerica",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Adams, L., and Macfarland, T. (1991). \"Testing for adjuncts.\" In Proceedings, Second Annual Meeting of the Formal Linguistics Society of Midamerica. Formal Linguistics Society of Midamerica.",
	"links": null
	},
	"BIBREF1": {
	"ref_id": "b1",
	"title": "Automatic acquisition of subcategorization frames from unrestricted English. Doctoral dissertation",
	"authors": [
	{
	"first": "M",
	"middle": [
	"R"
	],
	"last": "Brent",
	"suffix": ""
	}
	],
	"year": 1991,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Brent, M. R. (1991a). Automatic acquisition of subcategorization frames from unrestricted English. Doctoral dissertation, Massachusetts Institute of Technology, Cambridge MA.",
	"links": null
	},
	"BIBREF2": {
	"ref_id": "b2",
	"title": "Automatic acquisition of subcategorization frames from untagged text",
	"authors": [
	{
	"first": "M",
	"middle": [
	"R"
	],
	"last": "Brent",
	"suffix": ""
	}
	],
	"year": 1991,
	"venue": "Proceedings, 29th Annual Meeting of the ACL",
	"volume": "",
	"issue": "",
	"pages": "209--214",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Brent, M. R. (1991b). \"Automatic acquisition of subcategorization frames from untagged text.\" In Proceedings, 29th Annual Meeting of the ACL. 209-214.",
	"links": null
	},
	"BIBREF3": {
	"ref_id": "b3",
	"title": "A simple rule-based part of speech tagger",
	"authors": [
	{
	"first": "E",
	"middle": [],
	"last": "Brill",
	"suffix": ""
	}
	],
	"year": 1992,
	"venue": "Darpa Workshop on Speech and Natural Language",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Brill, E. (1992). \"A simple rule-based part of speech tagger.\" In Darpa Workshop on Speech and Natural Language. Harriman.",
	"links": null
	},
	"BIBREF4": {
	"ref_id": "b4",
	"title": "Automatically acquiring phrase structure using distributional analysis",
	"authors": [
	{
	"first": "E",
	"middle": [],
	"last": "Brill",
	"suffix": ""
	},
	{
	"first": "M",
	"middle": [],
	"last": "Marcus",
	"suffix": ""
	}
	],
	"year": 1992,
	"venue": "Darpa Workshop on Speech and Natural Language",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Brill, E., and Marcus, M. (1992). \"Automatically acquiring phrase structure using distributional analysis.\" In Darpa Workshop on Speech and Natural Language. Harriman.",
	"links": null
	},
	"BIBREF5": {
	"ref_id": "b5",
	"title": "Aspects of the Theory of Syntax",
	"authors": [
	{
	"first": "N",
	"middle": [],
	"last": "Chomsky",
	"suffix": ""
	}
	],
	"year": 1965,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Chomsky, N. (1965). Aspects of the Theory of Syntax. MIT Press.",
	"links": null
	},
	"BIBREF6": {
	"ref_id": "b6",
	"title": "A stochastic parts program and noun phrase parser for unrestricted text",
	"authors": [
	{
	"first": "K",
	"middle": [],
	"last": "Church",
	"suffix": ""
	}
	],
	"year": 1988,
	"venue": "Proceedings, Second ACL Conference on Applied NLP",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Church, K. (1988). \"A stochastic parts program and noun phrase parser for unrestricted text.\" In Proceedings, Second ACL Conference on Applied NLP.",
	"links": null
	},
	"BIBREF7": {
	"ref_id": "b7",
	"title": "Discovering planar segregations",
	"authors": [
	{
	"first": "T",
	"middle": [
	"M"
	],
	"last": "Ellison",
	"suffix": ""
	}
	],
	"year": 1991,
	"venue": "AAAI Spring Symposium on Machine Learning of Natural Language and Ontology",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Ellison, T. M. (1991). \"Discovering planar segregations.\" In AAAI Spring Symposium on Machine Learning of Natural Language and Ontology.",
	"links": null
	},
	"BIBREF8": {
	"ref_id": "b8",
	"title": "Bootstrapping syntactic categories using statistical methods",
	"authors": [
	{
	"first": "S",
	"middle": [],
	"last": "Finch",
	"suffix": ""
	},
	{
	"first": "N",
	"middle": [],
	"last": "Chater",
	"suffix": ""
	}
	],
	"year": 1992,
	"venue": "Background and Experiments in Machine Learning of Natural Language: Proceedings of the 1st SHOE Workshop. Katholieke Universiteit",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Finch, S., and Chater, N. (1992). \"Bootstrapping syntactic categories using statistical methods.\" In Background and Experiments in Machine Learning of Natural Language: Proceedings of the 1st SHOE Workshop. Katholieke Universiteit, Brabant, Holland.",
	"links": null
	},
	"BIBREF9": {
	"ref_id": "b9",
	"title": "Noun classification from predicate argument structures",
	"authors": [
	{
	"first": "D",
	"middle": [],
	"last": "Hindle",
	"suffix": ""
	}
	],
	"year": 1990,
	"venue": "Proceedings, 28th Annual Meeting of the ACL",
	"volume": "",
	"issue": "",
	"pages": "268--275",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Hindle, D. (1990). \"Noun classification from predicate argument structures.\" In Proceedings, 28th Annual Meeting of the ACL. 268-275.",
	"links": null
	},
	"BIBREF10": {
	"ref_id": "b10",
	"title": "Structural ambiguity and lexical relations",
	"authors": [
	{
	"first": "D",
	"middle": [],
	"last": "Hindle",
	"suffix": ""
	}
	],
	"year": 1991,
	"venue": "Proceedings, 29th Annual Meeting of the ACL",
	"volume": "",
	"issue": "",
	"pages": "229--236",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Hindle, D. (1991). \"Structural ambiguity and lexical relations.\" In Proceedings, 29th Annual Meeting of the ACL. 229-236.",
	"links": null
	},
	"BIBREF11": {
	"ref_id": "b11",
	"title": "X-bar Syntax: A Study of Phrase Structure",
	"authors": [
	{
	"first": "R",
	"middle": [],
	"last": "Jackendoff",
	"suffix": ""
	}
	],
	"year": 1977,
	"venue": "",
	"volume": "2",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Jackendoff, R. (1977). X-bar Syntax: A Study of Phrase Structure. Volume 2 of Linguistic Inquiry Monograph. MIT Press.",
	"links": null
	},
	"BIBREF12": {
	"ref_id": "b12",
	"title": "Probability and Statistical Inference",
	"authors": [
	{
	"first": "J",
	"middle": [
	"G"
	],
	"last": "Kalbfleisch",
	"suffix": ""
	}
	],
	"year": 1985,
	"venue": "",
	"volume": "2",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Kalbfleisch, J. G. (1985). Probability and Statistical Inference, Volume 2, Second Edition. Springer-Verlag.",
	"links": null
	},
	"BIBREF13": {
	"ref_id": "b13",
	"title": "KIMMO: A general morphological processor",
	"authors": [
	{
	"first": "L",
	"middle": [],
	"last": "Karttunen",
	"suffix": ""
	}
	],
	"year": 1983,
	"venue": "Texas Linguistics Forum",
	"volume": "22",
	"issue": "22",
	"pages": "165--186",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Karttunen, L. (1983). \"KIMMO: A general morphological processor.\" Texas Linguistics Forum, 22(22), 165-186.",
	"links": null
	},
	"BIBREF14": {
	"ref_id": "b14",
	"title": "A two-level morphological analysis of English",
	"authors": [
	{
	"first": "L",
	"middle": [],
	"last": "Karttunen",
	"suffix": ""
	},
	{
	"first": "K",
	"middle": [],
	"last": "Wittenburg",
	"suffix": ""
	}
	],
	"year": 1983,
	"venue": "Texas Linguistics Forum",
	"volume": "22",
	"issue": "22",
	"pages": "217--223",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Karttunen, L., and Wittenburg, K. (1983). \"A two-level morphological analysis of English.\" Texas Linguistics Forum, 22(22), 217-223.",
	"links": null
	},
	"BIBREF15": {
	"ref_id": "b15",
	"title": "How to Set Parameters",
	"authors": [
	{
	"first": "D",
	"middle": [
	"W"
	],
	"last": "Lightfoot",
	"suffix": ""
	}
	],
	"year": 1991,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Lightfoot, D. W. (1991). How to Set Parameters. MIT Press.",
	"links": null
	},
	"BIBREF16": {
	"ref_id": "b16",
	"title": "Parsing a natural language using mutual information statistics",
	"authors": [
	{
	"first": "D",
	"middle": [],
	"last": "Magerman",
	"suffix": ""
	},
	{
	"first": "M",
	"middle": [],
	"last": "Marcus",
	"suffix": ""
	}
	],
	"year": 1990,
	"venue": "Proceedings, Eighth National Conference on Arti~cial Intelligence",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Magerman, D., and Marcus, M. (1990). \"Parsing a natural language using mutual information statistics.\" In Proceedings, Eighth National Conference on Arti~cial Intelligence.",
	"links": null
	},
	"BIBREF17": {
	"ref_id": "b17",
	"title": "Distituent parsing and grammar induction",
	"authors": [
	{
	"first": "D",
	"middle": [],
	"last": "Magerman",
	"suffix": ""
	},
	{
	"first": "M",
	"middle": [],
	"last": "Marcus",
	"suffix": ""
	}
	],
	"year": 1991,
	"venue": "AAAI Spring Symposium on Machine Learning of Natural Language and Ontology",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Magerman, D., and Marcus, M. (1991). \"Distituent parsing and grammar induction.\" In AAAI Spring Symposium on Machine Learning of Natural Language and Ontology.",
	"links": null
	},
	"BIBREF18": {
	"ref_id": "b18",
	"title": "Insideoutside reestimation from partially bracketed corpora",
	"authors": [
	{
	"first": "F",
	"middle": [],
	"last": "Pereira",
	"suffix": ""
	},
	{
	"first": "Y",
	"middle": [],
	"last": "Schabes",
	"suffix": ""
	}
	],
	"year": 1992,
	"venue": "Proceedings",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Pereira, F., and Schabes, Y. (1992). \"Inside- outside reestimation from partially bracketed corpora.\" In Proceedings, 30th",
	"links": null
	},
	"BIBREF19": {
	"ref_id": "b19",
	"title": "Annual Meeting of the Association for Computational Linguistics",
	"authors": [],
	"year": null,
	"venue": "",
	"volume": "",
	"issue": "",
	"pages": "",
	"other_ids": {},
	"num": null,
	"urls": [],
	"raw_text": "Annual Meeting of the Association for Computational Linguistics.",
	"links": null
	}
	},
	"ref_entries": {
	"FIGREF0": {
	"num": null,
	"text": "infinitive). Each row shows the performance of the hypothesis-testing procedure for a different estimate P-s of the error-rate 7r_s. The first column shows the negative logarithm of P-s, which is varied from 5 (1 error in 32 occurrences) to 13 (1 error in 8192 occurrences). The second column shows P-s in decimal notation.The next four columns show the number of true positives (TP)--verbs judged +S both by machine and by hand; false positives (FP)--verbs judged +S by machine, -S by hand; true negatives (TN)--verbs judged -S both by machine and by hand; and false negatives (FN)--verbs judged -S by machine, +S by hand. The numbers represent distinct verbs, not occurrences. The seventh column shows the number of verbs that were misclassified (MC)--the sum of false positives and false negatives. The eighth column shows the percentage of verbs that were misclassified (%MC). The next-to-last column shows the precision (PRE)--the true positives divided by all verbs that Lerner judged to be +S. The final column shows the recall (REC)--the true positives divided by all verbs that were judged +S by hand.",
	"type_str": "figure",
	"uris": null
	},
	"FIGREF1": {
	"num": null,
	"text": "Figure 1",
	"type_str": "figure",
	"uris": null
	},
	"FIGREF2": {
	"num": null,
	"text": "-P(H [], N) area := H[0], rain-sum-of-squares := oo, best-estimate := I; Try each value of j from 1 to N as an estimate of jo for j from I tO N i] := area Estimate ~r-s by the average cooccurrence rate for the first j bins--those presumed to hold -S verbs P-s := p-s + (-~ , H'[i]) Check the fit, assuming j is the :kS boundary sum-of-squares := 0 for i from 0 to N Compute the predicted distribution/or bin i N! ~i /1 P := ~H-s~ -P-s) N-i Verbs in the first bins j and below are presumed -S if i<_j",
	"type_str": "figure",
	"uris": null
	},
	"TABREF0": {
	"content": "<table><tr><td>SF Description</td><td>Good Example</td><td>Bad Example</td></tr><tr><td>NP only</td><td>greet them</td><td>arrive them</td></tr><tr><td>tensed clause</td><td>hope he'll attend</td><td>want he'll attend</td></tr><tr><td>infinitive</td><td>hope to attend</td><td>greet to attend</td></tr><tr><td>NP & clause</td><td>tell him he's a fool</td><td>yell him he's a fool</td></tr><tr><td>NP & infinitive</td><td>want him to attend</td><td>hope him to attend</td></tr><tr><td>NP & NP</td><td>tell him the story</td><td>shout him the story</td></tr></table>",
	"num": null,
	"type_str": "table",
	"text": "The six syntactic frames studied in this paper.",
	"html": null
	},
	"TABREF1": {
	"content": "<table><tr><td>SUBJ:</td><td>I I he I she I we [ they</td></tr><tr><td>OBJ:</td><td>me [ him [ us [ them</td></tr><tr><td>SUBJ OBJ:</td><td>you [ it [ yours [ hers [ ours [ theirs</td></tr><tr><td>DET:</td><td>a [ an [</td></tr></table>",
	"num": null,
	"type_str": "table",
	"text": "Lexical categories used in the definitions of the cues. the [ her [ hiS [ its [ my",
	"html": null
	},
	"TABREF3": {
	"content": "<table><tr><td/><td colspan=\"3\">V NP NPNP NPcl NPinf cl inf</td><td/><td colspan=\"2\">V NP NPNP NPcl NPinf cl inf</td></tr><tr><td>recall</td><td>42</td><td>3</td><td>4</td><td>recur</td><td>5</td><td/></tr><tr><td>recede</td><td>5</td><td/><td/><td>redeem</td><td>3</td><td/></tr><tr><td>receive</td><td>106</td><td>4</td><td/><td>redirect</td><td>2</td><td/></tr><tr><td>reckon</td><td>10</td><td/><td/><td colspan=\"2\">rediscover 2</td><td/></tr><tr><td>recognize</td><td>71</td><td>6</td><td>6</td><td>reduce</td><td>85</td><td>2</td></tr><tr><td colspan=\"2\">recommend 32</td><td>2</td><td>1</td><td>reek</td><td>2</td><td/></tr><tr><td>reconcile</td><td>5</td><td/><td/><td>reel</td><td>2</td><td/></tr><tr><td>record</td><td>97</td><td>2</td><td>2</td><td>refer</td><td>43</td><td>1</td></tr><tr><td>recount</td><td>5</td><td/><td/><td>refine</td><td>4</td><td/></tr><tr><td>recover</td><td>14</td><td>4</td><td>1</td><td>reflect</td><td>41</td><td>1</td></tr><tr><td>recreate</td><td>2</td><td/><td/><td>refresh</td><td>4</td><td/></tr><tr><td>recruit</td><td>11</td><td/><td/><td>refuel</td><td>3</td><td/></tr><tr><td/><td/><td/><td/><td>refuse</td><td>22</td><td>1</td></tr></table>",
	"num": null,
	"type_str": "table",
	"text": "A sample of the data collected from the untagged Brown Corpus using the cues ofTable 3.",
	"html": null
	},
	"TABREF4": {
	"content": "<table/>",
	"num": null,
	"type_str": "table",
	"text": "",
	"html": null
	},
	"TABREF6": {
	"content": "<table><tr><td>-log 2 p-i,/</td><td>p-i,f</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>MC</td><td>%MC</td><td>PRE</td><td>REC</td></tr><tr><td>5</td><td>.0312</td><td>14</td><td>0</td><td>33</td><td>13</td><td>13</td><td>22</td><td>1.00</td><td>.52</td></tr><tr><td>6</td><td>.0156</td><td>16</td><td>0</td><td>33</td><td>11</td><td>11</td><td>18</td><td>1.00</td><td>.59</td></tr><tr><td>7</td><td>.0078</td><td>19</td><td>1</td><td>32</td><td>8</td><td>9</td><td>15</td><td>.95</td><td>.70</td></tr><tr><td>8</td><td>.0039</td><td>22</td><td>6</td><td>27</td><td>5</td><td>11</td><td>18</td><td>.79</td><td>.81</td></tr><tr><td>9</td><td>.0020</td><td>22</td><td>8</td><td>25</td><td>5</td><td>13</td><td>22</td><td>.73</td><td>.81</td></tr><tr><td>10</td><td>.0010</td><td>24</td><td>12</td><td>21</td><td>3</td><td>15</td><td>25</td><td>.67</td><td>.89</td></tr><tr><td>11</td><td>.0005</td><td>24</td><td>14</td><td>19</td><td>3</td><td>17</td><td>28</td><td>.63</td><td>.89</td></tr><tr><td>12</td><td>.0002</td><td>26</td><td>19</td><td>14</td><td>1</td><td>20</td><td>33</td><td>.58</td><td>.96</td></tr><tr><td>13</td><td>.0001</td><td>27</td><td>26</td><td>7</td><td>0</td><td>26</td><td>43</td><td>.51</td><td>1.00</td></tr></table>",
	"num": null,
	"type_str": "table",
	"text": "Comparison of automatic classification to hand judgments for infinitive complement, as a function of estimated error rate p (Brown Corpus).",
	"html": null
	},
	"TABREF7": {
	"content": "<table><tr><td>S</td><td>j</td><td>p-s</td><td>TP</td><td>FP</td><td>TN</td><td>FN</td><td>MC</td><td>%MC</td><td>PRE</td><td>REC</td></tr><tr><td>cl</td><td>2</td><td>0.0037</td><td>25</td><td>1</td><td>28</td><td>8</td><td>9</td><td>15</td><td>.96</td><td>.76</td></tr><tr><td>inf</td><td>2</td><td>0.0048</td><td>22</td><td>1</td><td>32</td><td>5</td><td>6</td><td>10</td><td>.96</td><td>.81</td></tr><tr><td>NPcl</td><td>1</td><td>0.0002</td><td>3</td><td>2</td><td>2</td><td>0</td><td>2</td><td>29</td><td>.60</td><td>1.00</td></tr><tr><td>NPinf</td><td>1</td><td>0.0005</td><td>5</td><td>0</td><td>3</td><td>2</td><td>2</td><td>20</td><td>1.00</td><td>.71</td></tr><tr><td>NPNP</td><td>3</td><td>0.0004</td><td>3</td><td>0</td><td>3</td><td>3</td><td>3</td><td>33</td><td>1.00</td><td>.50</td></tr><tr><td>NP</td><td>4</td><td>0.0132</td><td>52</td><td>1</td><td>5</td><td>59</td><td>60</td><td>51</td><td>.98</td><td>.47</td></tr><tr><td>total</td><td/><td/><td>110</td><td>5</td><td>73</td><td>74</td><td>79</td><td>30</td><td>.96</td><td>.60</td></tr></table>",
	"num": null,
	"type_str": "table",
	"text": "Comparison of automatic classification using the Brown Corpus to hand judgments. The estimate p-s is made with N = 100. The probability threshold is .02.",
	"html": null
	},
	"TABREF8": {
	"content": "<table><tr><td>project</td><td>52 board</td><td>111 operate</td><td>48 follow</td><td>76</td></tr><tr><td>projects</td><td>54 boards</td><td>31 operates</td><td>15 follows</td><td>72</td></tr><tr><td>projected</td><td>10 boarded</td><td>3 operated</td><td>26 followed</td><td>150</td></tr><tr><td>projecting</td><td>5 boarding</td><td colspan=\"2\">1 operating 55 following</td><td>97</td></tr></table>",
	"num": null,
	"type_str": "table",
	"text": "Distribution of occurrences among morphological forms in the BrownCorpus. The ambiguous words board and project show a pattern of distribution distinct from that of the unambiguous verbs operate and follow.",
	"html": null
	}
	}
	}
	}