Base_JSON/prefixP/json/P07/P07-1032.json

{
    "paper_id": "P07-1032",
    "header": {
        "generated_with": "S2ORC 1.0.0",
        "date_generated": "2023-01-19T08:51:03.205767Z"
    },
    "title": "Formalism-Independent Parser Evaluation with CCG and DepBank",
    "authors": [
        {
            "first": "Stephen",
            "middle": [],
            "last": "Clark",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "Oxford University Computing Laboratory Wolfson Building",
                "location": {
                    "addrLine": "Parks Road Oxford",
                    "postCode": "OX1 3QD",
                    "country": "UK"
                }
            },
            "email": "stephen.clark@comlab.ox.ac.uk"
        },
        {
            "first": "James",
            "middle": [
                "R"
            ],
            "last": "Curran",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "University of Sydney",
                "location": {
                    "postCode": "2006",
                    "region": "NSW",
                    "country": "Australia"
                }
            },
            "email": ""
        }
    ],
    "year": "",
    "venue": null,
    "identifiers": {},
    "abstract": "A key question facing the parsing community is how to compare parsers which use different grammar formalisms and produce different output. Evaluating a parser on the same resource used to create it can lead to non-comparable accuracy scores and an over-optimistic view of parser performance. In this paper we evaluate a CCG parser on DepBank, and demonstrate the difficulties in converting the parser output into Dep-Bank grammatical relations. In addition we present a method for measuring the effectiveness of the conversion, which provides an upper bound on parsing accuracy. The CCG parser obtains an F-score of 81.9% on labelled dependencies, against an upper bound of 84.8%. We compare the CCG parser against the RASP parser, outperforming RASP by over 5% overall and on the majority of dependency types.",
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        "abstract": [
            {
                "text": "A key question facing the parsing community is how to compare parsers which use different grammar formalisms and produce different output. Evaluating a parser on the same resource used to create it can lead to non-comparable accuracy scores and an over-optimistic view of parser performance. In this paper we evaluate a CCG parser on DepBank, and demonstrate the difficulties in converting the parser output into Dep-Bank grammatical relations. In addition we present a method for measuring the effectiveness of the conversion, which provides an upper bound on parsing accuracy. The CCG parser obtains an F-score of 81.9% on labelled dependencies, against an upper bound of 84.8%. We compare the CCG parser against the RASP parser, outperforming RASP by over 5% overall and on the majority of dependency types.",
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                "section": "Abstract",
                "sec_num": null
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                "text": "Parsers have been developed for a variety of grammar formalisms, for example HPSG (Toutanova et al., 2002; Malouf and van Noord, 2004) , LFG (Kaplan et al., 2004; Cahill et al., 2004) , TAG (Sarkar and Joshi, 2003) , CCG (Hockenmaier and Steedman, 2002; Clark and Curran, 2004b) , and variants of phrase-structure grammar , including the phrase-structure grammar implicit in the Penn Treebank (Collins, 2003; Charniak, 2000) . Different parsers produce different output, for ex-ample phrase structure trees (Collins, 2003) , dependency trees (Nivre and Scholz, 2004) , grammatical relations , and formalismspecific dependencies (Clark and Curran, 2004b) . This variety of formalisms and output creates a challenge for parser evaluation.",
                "cite_spans": [
                    {
                        "start": 82,
                        "end": 106,
                        "text": "(Toutanova et al., 2002;",
                        "ref_id": "BIBREF20"
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                    {
                        "start": 107,
                        "end": 134,
                        "text": "Malouf and van Noord, 2004)",
                        "ref_id": "BIBREF15"
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                    {
                        "start": 141,
                        "end": 162,
                        "text": "(Kaplan et al., 2004;",
                        "ref_id": "BIBREF13"
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                        "start": 163,
                        "end": 183,
                        "text": "Cahill et al., 2004)",
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                        "start": 186,
                        "end": 189,
                        "text": "TAG",
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                        "start": 190,
                        "end": 214,
                        "text": "(Sarkar and Joshi, 2003)",
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                        "start": 221,
                        "end": 253,
                        "text": "(Hockenmaier and Steedman, 2002;",
                        "ref_id": "BIBREF11"
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                    {
                        "start": 254,
                        "end": 278,
                        "text": "Clark and Curran, 2004b)",
                        "ref_id": "BIBREF9"
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                    {
                        "start": 393,
                        "end": 408,
                        "text": "(Collins, 2003;",
                        "ref_id": null
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                        "start": 409,
                        "end": 424,
                        "text": "Charniak, 2000)",
                        "ref_id": "BIBREF7"
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                        "start": 507,
                        "end": 522,
                        "text": "(Collins, 2003)",
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                    {
                        "start": 542,
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                        "text": "(Nivre and Scholz, 2004)",
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                        "text": "(Clark and Curran, 2004b)",
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                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "The majority of parser evaluations have used test sets drawn from the same resource used to develop the parser. This allows the many parsers based on the Penn Treebank, for example, to be meaningfully compared. However, there are two drawbacks to this approach. First, parser evaluations using different resources cannot be compared; for example, the Parseval scores obtained by Penn Treebank parsers cannot be compared with the dependency F-scores obtained by evaluating on the Parc Dependency Bank. Second, using the same resource for development and testing can lead to an over-optimistic view of parser performance.",
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                "section": "Introduction",
                "sec_num": "1"
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                "text": "In this paper we evaluate a CCG parser (Clark and Curran, 2004b) on the Briscoe and Carroll version of DepBank . The CCG parser produces head-dependency relations, so evaluating the parser should simply be a matter of converting the CCG dependencies into those in Dep-Bank. Such conversions have been performed for other parsers, including parsers producing phrase structure output (Kaplan et al., 2004; Preiss, 2003) . However, we found that performing such a conversion is a time-consuming and non-trivial task.",
                "cite_spans": [
                    {
                        "start": 39,
                        "end": 64,
                        "text": "(Clark and Curran, 2004b)",
                        "ref_id": "BIBREF9"
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                    {
                        "start": 382,
                        "end": 403,
                        "text": "(Kaplan et al., 2004;",
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                "section": "Introduction",
                "sec_num": "1"
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                "text": "The contributions of this paper are as follows. First, we demonstrate the considerable difficulties associated with formalism-independent parser evaluation, highlighting the problems in converting the output of a parser from one representation to another. Second, we develop a method for measuring how effective the conversion process is, which also provides an upper bound for the performance of the parser, given the conversion process being used; this method can be adapted by other researchers to strengthen their own parser comparisons. And third, we provide the first evaluation of a widecoverage CCG parser outside of CCGbank, obtaining impressive results on DepBank and outperforming the RASP parser by over 5% overall and on the majority of dependency types.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
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            {
                "text": "The most common form of parser evaluation is to apply the Parseval metrics to phrase-structure parsers based on the Penn Treebank, and the highest reported scores are now over 90% (Bod, 2003; Charniak and Johnson, 2005) . However, it is unclear whether these high scores accurately reflect the performance of parsers in applications. It has been argued that the Parseval metrics are too forgiving and that phrase structure is not the ideal representation for a gold standard (Carroll et al., 1998) . Also, using the same resource for training and testing may result in the parser learning systematic errors which are present in both the training and testing material. An example of this is from CCGbank (Hockenmaier, 2003) , where all modifiers in noun-noun compound constructions modify the final noun (because the Penn Treebank, from which CCGbank is derived, does not contain the necessary information to obtain the correct bracketing). Thus there are nonnegligible, systematic errors in both the training and testing material, and the CCG parsers are being rewarded for following particular mistakes.",
                "cite_spans": [
                    {
                        "start": 180,
                        "end": 191,
                        "text": "(Bod, 2003;",
                        "ref_id": "BIBREF0"
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                    {
                        "start": 192,
                        "end": 219,
                        "text": "Charniak and Johnson, 2005)",
                        "ref_id": "BIBREF6"
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                    {
                        "start": 475,
                        "end": 497,
                        "text": "(Carroll et al., 1998)",
                        "ref_id": "BIBREF5"
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                    {
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                        "text": "(Hockenmaier, 2003)",
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                ],
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                "eq_spans": [],
                "section": "Previous Work",
                "sec_num": "2"
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                "text": "There are parser evaluation suites which have been designed to be formalism-independent and which have been carefully and manually corrected. Carroll et al. (1998) describe such a suite, consisting of sentences taken from the Susanne corpus, annotated with Grammatical Relations (GRs) which specify the syntactic relation between a head and dependent. Thus all that is required to use such a scheme, in theory, is that the parser being evaluated is able to identify heads. A similar resource -the Parc Dependency Bank (DepBank) (King et al., 2003) -has been created using sentences from the Penn Treebank. reannotated this resource using their GRs scheme, and used it to evaluate the RASP parser. Kaplan et al. (2004) compare the Collins (2003) parser with the Parc LFG parser by mapping LFG Fstructures and Penn Treebank parses into DepBank dependencies, claiming that the LFG parser is considerably more accurate with only a slight reduction in speed. Preiss (2003) compares the parsers of Collins (2003) and Charniak (2000) , the GR finder of Buchholz et al. (1999) , and the RASP parser, using the Carroll et al. (1998) gold-standard. The Penn Treebank trees of the Collins and Charniak parsers, and the GRs of the Buchholz parser, are mapped into the required GRs, with the result that the GR finder of Buchholz is the most accurate.",
                "cite_spans": [
                    {
                        "start": 142,
                        "end": 163,
                        "text": "Carroll et al. (1998)",
                        "ref_id": "BIBREF5"
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                        "start": 528,
                        "end": 547,
                        "text": "(King et al., 2003)",
                        "ref_id": "BIBREF14"
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                        "start": 697,
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                        "text": "Kaplan et al. (2004)",
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                        "start": 954,
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                        "text": "Preiss (2003)",
                        "ref_id": "BIBREF17"
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                        "start": 992,
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                        "text": "Collins (2003)",
                        "ref_id": null
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                    {
                        "start": 1011,
                        "end": 1026,
                        "text": "Charniak (2000)",
                        "ref_id": "BIBREF7"
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                    {
                        "start": 1046,
                        "end": 1068,
                        "text": "Buchholz et al. (1999)",
                        "ref_id": "BIBREF3"
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                    {
                        "start": 1102,
                        "end": 1123,
                        "text": "Carroll et al. (1998)",
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                "section": "Previous Work",
                "sec_num": "2"
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            {
                "text": "The major weakness of these evaluations is that there is no measure of the difficultly of the conversion process for each of the parsers. Kaplan et al. (2004) clearly invested considerable time and expertise in mapping the output of the Collins parser into the DepBank dependencies, but they also note that \"This conversion was relatively straightforward for LFG structures . . . However, a certain amount of skill and intuition was required to provide a fair conversion of the Collins trees\". Without some measure of the difficulty -and effectiveness -of the conversion, there remains a suspicion that the Collins parser is being unfairly penalised.",
                "cite_spans": [
                    {
                        "start": 138,
                        "end": 158,
                        "text": "Kaplan et al. (2004)",
                        "ref_id": "BIBREF13"
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                "eq_spans": [],
                "section": "Previous Work",
                "sec_num": "2"
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            {
                "text": "One way of providing such a measure is to convert the original gold standard on which the parser is based and evaluate that against the new gold standard (assuming the two resources are based on the same corpus). In the case of Kaplan et al. (2004) , the testing procedure would include running their conversion process on Section 23 of the Penn Treebank and evaluating the output against DepBank. As well as providing some measure of the effectiveness of the conversion, this method would also provide an upper bound for the Collins parser, giving the score that a perfect Penn Treebank parser would obtain on DepBank (given the conversion process).",
                "cite_spans": [
                    {
                        "start": 228,
                        "end": 248,
                        "text": "Kaplan et al. (2004)",
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                "section": "Previous Work",
                "sec_num": "2"
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            {
                "text": "We perform such an evaluation for the CCG parser, with the surprising result that the upper bound on DepBank is only 84.8%, despite the considerable effort invested in developing the conversion process.",
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                "sec_num": "2"
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                "text": "3 The CCG Parser Clark and Curran (2004b) describes the CCG parser used for the evaluation. The grammar used by the parser is extracted from CCGbank, a CCG version of the Penn Treebank (Hockenmaier, 2003) . The grammar consists of 425 lexical categories -expressing subcategorisation information -plus a small number of combinatory rules which combine the categories (Steedman, 2000) . A supertagger first assigns lexical categories to the words in a sentence, which are then combined by the parser using the combinatory rules and the CKY algorithm. A log-linear model scores the alternative parses. We use the normal-form model, which assigns probabilities to single derivations based on the normal-form derivations in CCGbank. The features in the model are defined over local parts of the derivation and include word-word dependencies. A packed chart representation allows efficient decoding, with the Viterbi algorithm finding the most probable derivation.",
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                    {
                        "start": 17,
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                        "text": "Clark and Curran (2004b)",
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                        "start": 185,
                        "end": 204,
                        "text": "(Hockenmaier, 2003)",
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                        "start": 367,
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                        "text": "(Steedman, 2000)",
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                ],
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                "section": "Previous Work",
                "sec_num": "2"
            },
            {
                "text": "The parser outputs predicate-argument dependencies defined in terms of CCG lexical categories. More formally, a CCG predicate-argument dependency is a 5-tuple:",
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                "sec_num": "2"
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            {
                "text": "h f , f, s, h a , l",
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                "eq_spans": [],
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                "sec_num": "2"
            },
            {
                "text": ", where h f is the lexical item of the lexical category expressing the dependency relation; f is the lexical category; s is the argument slot; h a is the head word of the argument; and l encodes whether the dependency is long-range. For example, the dependency encoding company as the object of bought (as in IBM bought the company) is represented as follows:",
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                "sec_num": "2"
            },
            {
                "text": "EQUATION",
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                "eq_spans": [
                    {
                        "start": 0,
                        "end": 8,
                        "text": "EQUATION",
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                        "raw_str": "bought, (S \\NP 1 )/NP 2 , 2, company, \u2212",
                        "eq_num": "(1)"
                    }
                ],
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                "sec_num": "2"
            },
            {
                "text": "The lexical category (S \\NP 1 )/NP 2 is the category of a transitive verb, with the first argument slot corresponding to the subject, and the second argument slot corresponding to the direct object. The final field indicates the nature of any long-range dependency; in (1) the dependency is local.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Previous Work",
                "sec_num": "2"
            },
            {
                "text": "The predicate-argument dependencies -including long-range dependencies -are encoded in the lexicon by adding head and dependency annotation to the lexical categories. For example, the expanded category for the control verb persuade",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Previous Work",
                "sec_num": "2"
            },
            {
                "text": "is (((S [dcl] persuade \\NP 1 )/(S [to] 2 \\NP X ))/NP X,3 )",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Previous Work",
                "sec_num": "2"
            },
            {
                "text": ". Numerical subscripts on the argument categories represent dependency relations; the head of the final declarative sentence is persuade; and the head of the infinitival complement's subject is identified with the head of the object, using the variable X, as in standard unification-based accounts of control.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Previous Work",
                "sec_num": "2"
            },
            {
                "text": "Previous evaluations of CCG parsers have used the predicate-argument dependencies from CCGbank as a test set (Hockenmaier and Steedman, 2002; Clark and Curran, 2004b) , with impressive results of over 84% F-score on labelled dependencies. In this paper we reinforce the earlier results with the first evaluation of a CCG parser outside of CCGbank.",
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                        "start": 109,
                        "end": 141,
                        "text": "(Hockenmaier and Steedman, 2002;",
                        "ref_id": "BIBREF11"
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                        "start": 142,
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                        "text": "Clark and Curran, 2004b)",
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                ],
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                "section": "Previous Work",
                "sec_num": "2"
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            {
                "text": "For the gold standard we chose the version of Dep-Bank reannotated by , consisting of 700 sentences from Section 23 of the Penn Treebank. The B&C scheme is similar to the original DepBank scheme (King et al., 2003) , but overall contains less grammatical detail; describes the differences. We chose this resource for the following reasons: it is publicly available, allowing other researchers to compare against our results; the GRs making up the annotation share some similarities with the predicateargument dependencies output by the CCG parser; and we can directly compare our parser against a non-CCG parser, namely the RASP parser. We chose not to use the corpus based on the Susanne corpus (Carroll et al., 1998) because the GRs are less like the CCG dependencies; the corpus is not based on the Penn Treebank, making comparison more difficult because of tokenisation differences, for example; and the latest results for RASP are on DepBank.",
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                        "start": 195,
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                        "text": "(King et al., 2003)",
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                        "text": "(Carroll et al., 1998)",
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                ],
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                "eq_spans": [],
                "section": "Dependency Conversion to DepBank",
                "sec_num": "4"
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            {
                "text": "The GRs are described in and . Table 1 lists the GRs used in the evaluation. As an example, the sentence The parent sold Imperial produces three GRs: (det parent The), (ncsubj sold parent ) and (dobj sold Imperial). Note that some GRs -in this example ncsubj -have a subtype slot, giving extra information. The subtype slot for ncsubj is used to indicate passive subjects, with the null value \" \" for active subjects and obj for passive subjects. Other subtype slots are discussed in Section 4.2.",
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                "ref_spans": [
                    {
                        "start": 31,
                        "end": 38,
                        "text": "Table 1",
                        "ref_id": "TABREF1"
                    }
                ],
                "eq_spans": [],
                "section": "Dependency Conversion to DepBank",
                "sec_num": "4"
            },
            {
                "text": "The CCG dependencies were transformed into GRs in two stages. GRs. This involved mapping each argument slot in the 425 lexical categories in the CCG lexicon onto a GR. In the second stage, the GRs created from the parser output were post-processed to correct some of the obvious remaining differences between the CCG and GR representations.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Dependency Conversion to DepBank",
                "sec_num": "4"
            },
            {
                "text": "In the process of performing the transformation we encountered a methodological problem: without looking at examples it was difficult to create the mapping and impossible to know whether the two representations were converging. split the 700 sentences in DepBank into a test and development set, but the latter only consists of 140 sentences which was not enough to reliably create the transformation. There are some development files in the RASP release which provide examples of the GRs, which were used when possible, but these only cover a subset of the CCG lexical categories.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Dependency Conversion to DepBank",
                "sec_num": "4"
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            {
                "text": "Our solution to this problem was to convert the gold standard dependencies from CCGbank into GRs and use these to develop the transformation. So we did inspect the annotation in DepBank, and compared it to the transformed CCG dependencies, but only the gold-standard CCG dependencies. Thus the parser output was never used during this process. We also ensured that the dependency mapping and the post processing are general to the GRs scheme and not specific to the test set or parser. Creating the dependency transformation is more difficult than these examples suggest. The first problem is that the mapping from CCG dependencies to GRs is many-to-many. For example, the transitive verb category (S [dcl ]\\NP )/NP applies to the copula in sentences like Imperial Corp. is the parent of Imperial Savings & Loan. With the default annotation, the relation between is and parent would be dobj, whereas in DepBank the argument of the copula is analysed as an xcomp. Table 3 gives some examples of how we attempt to deal with this problem. The constraint in the first example means that, whenever the word associated with the transitive verb category is a form of be, the second argument is xcomp, otherwise the default case applies (in this case dobj). There are a number of categories with similar constraints, checking whether the word associated with the category is a form of be.",
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                "ref_spans": [
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                        "start": 963,
                        "end": 970,
                        "text": "Table 3",
                        "ref_id": null
                    }
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                "eq_spans": [],
                "section": "Dependency Conversion to DepBank",
                "sec_num": "4"
            },
            {
                "text": "(S [dcl ]\\NP1 )/NP2 1 (ncsubj %l %f ) (S [dcl ]\\NP1 )/NP2 2 (dobj %l %f) (S \\NP )/(S \\NP )1 1 (ncmod %f %l) (NP \\NP1 )/NP2 1 (ncmod %f %l) (NP \\NP1 )/NP2 2 (dobj %l %f) NP [nb]/N1 1 (det %f %l) (NP \\NP1 )/(S [pss]\\NP )2 1 (xmod %f %l) (NP \\NP1 )/(S [pss]\\NP )2 2 (xcomp %l %f) ((S \\NP )\\(S \\NP )1 )/S [dcl ]2 1 (cmod %f %l) ((S \\NP )\\(S \\NP )1 )/S [dcl ]2 2 (ccomp %l %f) ((S [dcl ]\\NP1 )/NP2 )/NP3 2 (obj2 %l %f) (S [dcl ]\\NP1 )/(S [b]\\NP )2 2 (aux %f %l)",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Mapping the CCG dependencies to GRs",
                "sec_num": "4.1"
            },
            {
                "text": "The second type of constraint, shown in the third line of the table, checks the lexical category of the word filling the argument slot. In this example, if the lexical category of the preposition is PP /NP , then the second argument of (S [dcl ]\\NP )/PP maps to iobj; thus in The loss stems from several factors the relation between the verb and preposition is (iobj stems from). If the lexical category of 251 Table 3 : Examples of the many-to-many nature of the CCG dependency to GRs mapping, and a ternary GR the preposition is PP /(S [ng]\\NP ), then the GR is xcomp; thus in The future depends on building ties the relation between the verb and preposition is (xcomp depends on). There are a number of CCG dependencies with similar constraints, many of them covering the iobj/xcomp distinction.",
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                "ref_spans": [
                    {
                        "start": 411,
                        "end": 418,
                        "text": "Table 3",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Mapping the CCG dependencies to GRs",
                "sec_num": "4.1"
            },
            {
                "text": "The second difficulty is that not all the GRs are binary relations, whereas the CCG dependencies are all binary. The primary example of this is to-infinitival constructions. For example, in the sentence The company wants to wean itself away from expensive gimmicks, the CCG parser produces two dependencies relating wants, to and wean, whereas there is only one GR: (xcomp to wants wean). The final row of Table 3 gives an example. We implement this constraint by introducing a %k variable into the GR template which denotes the argument of the category in the constraint column (which, as before, is the lexical category of the word filling the argument slot). In the example, the current category is ( The %k variable allows us to look beyond the arguments of the current category when creating the GRs.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 406,
                        "end": 413,
                        "text": "Table 3",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Mapping the CCG dependencies to GRs",
                "sec_num": "4.1"
            },
            {
                "text": "A further difficulty is that the head passing conventions differ between DepBank and CCGbank. By head passing we mean the mechanism which determines the heads of constituents and the mechanism by which words become arguments of longrange dependencies. For example, in the sentence The group said it would consider withholding royalty payments, the DepBank and CCGbank annotations create a dependency between said and the following clause. However, in DepBank the relation is between said and consider, whereas in CCGbank the relation is between said and would. We fixed this problem by defining the head of would consider to be consider rather than would, by changing the annotation of all the relevant lexical categories in the CCG lexicon (mainly those creating aux relations).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Mapping the CCG dependencies to GRs",
                "sec_num": "4.1"
            },
            {
                "text": "There are more subject relations in CCGbank than DepBank. In the previous example, CCGbank has a subject relation between it and consider, and also it and would, whereas DepBank only has the relation between it and consider. In practice this means ignoring a number of the subject dependencies output by the CCG parser.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Mapping the CCG dependencies to GRs",
                "sec_num": "4.1"
            },
            {
                "text": "Another example where the dependencies differ is the treatment of relative pronouns. For example, in Sen. Mitchell, who had proposed the streamlining, the subject of proposed is Mitchell in CCGbank but who in DepBank. Again, we implemented this change by fixing the head annotation in the lexical categories which apply to relative pronouns.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Mapping the CCG dependencies to GRs",
                "sec_num": "4.1"
            },
            {
                "text": "To obtain some idea of whether the schemes were converging, we performed the following oracle experiment. We took the CCG derivations from CCGbank corresponding to the sentences in Dep-Bank, and forced the parser to produce goldstandard derivations, outputting the newly created GRs. Treating the DepBank GRs as a gold-standard, and comparing these with the CCGbank GRs, gave precision and recall scores of only 76.23% and 79.56% respectively (using the RASP evaluation tool). Thus given the current mapping, the perfect CCGbank parser would achieve an F-score of only 77.86% when evaluated against DepBank.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "On inspecting the output, it was clear that a number of general rules could be applied to bring the schemes closer together, which was implemented as a post-processing script. The first set of changes deals with coordination. One significant difference between DepBank and CCGbank is the treatment of coordinations as arguments. Consider the example The president and chief executive officer said the loss stems from several factors. For both DepBank and the transformed CCGbank there are two conj GRs arising from the coordination: (conj and president) and (conj and officer). The difference arises in the subject of said: in DepBank the subject is and: (ncsubj said and ), whereas in CCGbank there are two subjects: (ncsubj said president ) and (ncsubj said officer ). We deal with this difference by replacing any pairs of GRs which differ only in their arguments, and where the arguments are coordinated items, with a single GR containing the coordination term as the argument.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "Ampersands are a frequently occurring problem in WSJ text. For example, the CCGbank analysis of Standard & Poor's index assigns the lexical category N /N to both Standard and &, treating them as modifiers of Poor, whereas DepBank treats & as a coordinating term. We fixed this by creating conj GRs between any & and the two words either side; removing the modifier GR between the two words; and replacing any GRs in which the words either side of the & are arguments with a single GR in which & is the argument.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "The ta relation, which identifies text adjuncts delimited by punctuation, is difficult to assign correctly to the parser output. The simple punctuation rules used by the parser do not contain enough information to distinguish between the various cases of ta. Thus the only rule we have implemented, which is somewhat specific to the newspaper genre, is to replace GRs of the form (cmod say arg) with (ta quote arg say), where say can be any of say, said or says. This rule applies to only a small subset of the ta cases but has high enough precision to be worthy of inclusion.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "A common source of error is the distinction between iobj and ncmod, which is not surprising given the difficulty that human annotators have in distinguishing arguments and adjuncts. There are many cases where an argument in DepBank is an adjunct in CCGbank, and vice versa. The only change we have made is to turn all ncmod GRs with of as the modifier into iobj GRs (unless the ncmod is a partitive predeterminer). This was found to have high precision and applies to a large number of cases.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "There are some dependencies in CCGbank which do not appear in DepBank. Examples include any dependencies in which a punctuation mark is one of the arguments; these were removed from the output.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "We attempt to fill the subtype slot for some GRs.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "The subtype slot specifies additional information about the GR; examples include the value obj in a passive ncsubj, indicating that the subject is an underlying object; the value num in ncmod, indicating a numerical quantity; and prt in ncmod to indicate a verb particle. The passive case is identified as follows: any lexical category which starts S [pss]\\NP indicates a passive verb, and we also mark any verbs POS tagged VBN and assigned the lexical category N /N as passive. Both these rules have high precision, but still leave many of the cases in DepBank unidentified. The numerical case is identified using two rules: the num subtype is added if any argument in a GR is assigned the lexical category N /N [num], and if any of the arguments in an ncmod is POS tagged CD. prt is added to an ncmod if the modifiee has any of the verb POS tags and if the modifier has POS tag RP. The final columns of Table 4 show the accuracy of the transformed gold-standard CCGbank dependencies when compared with DepBank; the simple post-processing rules have increased the F-score from 77.86% to 84.76%. This F-score is an upper bound on the performance of the CCG parser.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 905,
                        "end": 912,
                        "text": "Table 4",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Post processing of the GR output",
                "sec_num": "4.2"
            },
            {
                "text": "The results in Table 4 were obtained by parsing the sentences from CCGbank corresponding to those in the 560-sentence test set used by . We used the CCGbank sentences because these differ in some ways to the original Penn Treebank sentences (there are no quotation marks in CCGbank, for example) and the parser has been trained on CCGbank. Even here we experienced some unexpected difficulties, since some of the tokenisation is different between DepBank and CCGbank and there are some sentences in DepBank which have been significantly shortened compared to the original Penn Treebank sentences. We modified the CCGbank sentences -and the CCGbank analyses since these were used for the oracle experiments -to be as close to the DepBank sentences as possible. All the results were obtained using the RASP evaluation scripts, with the results for the RASP parser taken from . The results for CCGbank were obtained using the oracle method described above. Table 4 : Accuracy on DepBank. F-score is the balanced harmonic mean of precision (P ) and recall (R): 2P R/(P + R). # GRs is the number of GRs in DepBank.",
                "cite_spans": [],
                "ref_spans": [
                    {
                        "start": 15,
                        "end": 22,
                        "text": "Table 4",
                        "ref_id": null
                    },
                    {
                        "start": 954,
                        "end": 961,
                        "text": "Table 4",
                        "ref_id": null
                    }
                ],
                "eq_spans": [],
                "section": "Results",
                "sec_num": "5"
            },
            {
                "text": "The CCG parser results are based on automatically assigned POS tags, using the Curran and tagger. The coverage of the parser on Dep-Bank is 100%. For a GR in the parser output to be correct, it has to match the gold-standard GR exactly, including any subtype slots; however, it is possible for a GR to be incorrect at one level but correct at a subsuming level. 1 For example, if an ncmod GR is incorrectly labelled with xmod, but is otherwise correct, it will be correct for all levels which subsume both ncmod and xmod, for example mod. The microaveraged scores are calculated by aggregating the counts for all the relations in the hierarchy, including the subsuming relations; the macro-averaged scores are the mean of the individual scores for each relation .",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Results",
                "sec_num": "5"
            },
            {
                "text": "The results show that the performance of the CCG parser is higher than RASP overall, and also higher on the majority of GR types (especially the more frequent types). RASP uses an unlexicalised parsing model and has not been tuned to newspaper text. On the other hand it has had many years of development; thus it provides a strong baseline for this test set. The overall F-score for the CCG parser, 81.86%, is only 3 points below that for CCGbank, which pro-vides an upper bound for the CCG parser (given the conversion process being used).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Results",
                "sec_num": "5"
            },
            {
                "text": "A contribution of this paper has been to highlight the difficulties associated with cross-formalism parser comparison. Note that the difficulties are not unique to CCG, and many would apply to any crossformalism comparison, especially with parsers using automatically extracted grammars. Parser evaluation has improved on the original Parseval measures (Carroll et al., 1998) , but the challenge remains to develop a representation and evaluation suite which can be easily applied to a wide variety of parsers and formalisms. Despite the difficulties, we have given the first evaluation of a CCG parser outside of CCGbank, outperforming the RASP parser by over 5% overall and on the majority of dependency types.",
                "cite_spans": [
                    {
                        "start": 353,
                        "end": 375,
                        "text": "(Carroll et al., 1998)",
                        "ref_id": "BIBREF5"
                    }
                ],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusion",
                "sec_num": "6"
            },
            {
                "text": "Can the CCG parser be compared with parsers other than RASP? give a rough comparison of RASP with the Parc LFG parser on the different versions of DepBank, obtaining similar results overall, but they acknowledge that the results are not strictly comparable because of the different annotation schemes used. Comparison with Penn Treebank parsers would be difficult because, for many constructions, the Penn Treebank trees and 254 CCG derivations are different shapes, and reversing the mapping Hockenmaier used to create CCGbank would be very difficult. Hence we challenge other parser developers to map their own parse output into the version of DepBank used here.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusion",
                "sec_num": "6"
            },
            {
                "text": "One aspect of parser evaluation not covered in this paper is efficiency. The CCG parser took only 22.6 seconds to parse the 560 sentences in DepBank, with the accuracy given earlier. Using a cluster of 18 machines we have also parsed the entire Gigaword corpus in less than five days. Hence, we conclude that accurate, large-scale, linguistically-motivated NLP is now practical with CCG.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusion",
                "sec_num": "6"
            },
            {
                "text": "The GRs are arranged in a hierarchy, with those inTable 1at the leaves; a small number of more general GRs subsume these.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "We would like to thanks the anonymous reviewers for their helpful comments. James Curran was funded under ARC Discovery grants DP0453131 and DP0665973.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgements",
                "sec_num": null
            }
        ],
        "bib_entries": {
            "BIBREF0": {
                "ref_id": "b0",
                "title": "An efficient implementation of a new DOP model",
                "authors": [
                    {
                        "first": "Rens",
                        "middle": [],
                        "last": "Bod",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "Proceedings of the 10th Meeting of the EACL",
                "volume": "",
                "issue": "",
                "pages": "19--26",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Rens Bod. 2003. An efficient implementation of a new DOP model. In Proceedings of the 10th Meeting of the EACL, pages 19-26, Budapest, Hungary.",
                "links": null
            },
            "BIBREF1": {
                "ref_id": "b1",
                "title": "Evaluating the accuracy of an unlexicalized statistical parser on the PARC DepBank",
                "authors": [
                    {
                        "first": "Ted",
                        "middle": [],
                        "last": "Briscoe",
                        "suffix": ""
                    },
                    {
                        "first": "John",
                        "middle": [],
                        "last": "Carroll",
                        "suffix": ""
                    }
                ],
                "year": 2006,
                "venue": "Proceedings of the Poster Session of COLING/ACL-06",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Ted Briscoe and John Carroll. 2006. Evaluating the accuracy of an unlexicalized statistical parser on the PARC DepBank. In Proceedings of the Poster Session of COLING/ACL-06, Sydney, Australia.",
                "links": null
            },
            "BIBREF2": {
                "ref_id": "b2",
                "title": "The second release of the RASP system",
                "authors": [
                    {
                        "first": "Ted",
                        "middle": [],
                        "last": "Briscoe",
                        "suffix": ""
                    },
                    {
                        "first": "John",
                        "middle": [],
                        "last": "Carroll",
                        "suffix": ""
                    },
                    {
                        "first": "Rebecca",
                        "middle": [],
                        "last": "Watson",
                        "suffix": ""
                    }
                ],
                "year": 2006,
                "venue": "Proceedings of the Interactive Demo Session of COLING/ACL-06",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Ted Briscoe, John Carroll, and Rebecca Watson. 2006. The second release of the RASP system. In Proceedings of the Interactive Demo Session of COLING/ACL-06, Sydney, Australia.",
                "links": null
            },
            "BIBREF3": {
                "ref_id": "b3",
                "title": "Cascaded grammatical relation assignment",
                "authors": [
                    {
                        "first": "Sabine",
                        "middle": [],
                        "last": "Buchholz",
                        "suffix": ""
                    },
                    {
                        "first": "Jorn",
                        "middle": [],
                        "last": "Veenstra",
                        "suffix": ""
                    },
                    {
                        "first": "Walter",
                        "middle": [],
                        "last": "Daelemans",
                        "suffix": ""
                    }
                ],
                "year": 1999,
                "venue": "Proceedings of EMNLP/VLC-99",
                "volume": "",
                "issue": "",
                "pages": "239--246",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Sabine Buchholz, Jorn Veenstra, and Walter Daelemans. 1999. Cascaded grammatical relation assignment. In Proceedings of EMNLP/VLC-99, pages 239-246, University of Mary- land, June 21-22.",
                "links": null
            },
            "BIBREF4": {
                "ref_id": "b4",
                "title": "Long-distance dependency resolution in automatically acquired wide-coverage PCFG-based LFG approximations",
                "authors": [
                    {
                        "first": "A",
                        "middle": [],
                        "last": "Cahill",
                        "suffix": ""
                    },
                    {
                        "first": "M",
                        "middle": [],
                        "last": "Burke",
                        "suffix": ""
                    },
                    {
                        "first": "R",
                        "middle": [],
                        "last": "O'donovan",
                        "suffix": ""
                    },
                    {
                        "first": "J",
                        "middle": [],
                        "last": "Van Genabith",
                        "suffix": ""
                    },
                    {
                        "first": "A",
                        "middle": [],
                        "last": "Way",
                        "suffix": ""
                    }
                ],
                "year": 2004,
                "venue": "Proceedings of the 42nd Meeting of the ACL",
                "volume": "",
                "issue": "",
                "pages": "320--327",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "A. Cahill, M. Burke, R. O'Donovan, J. van Genabith, and A. Way. 2004. Long-distance dependency resolution in au- tomatically acquired wide-coverage PCFG-based LFG ap- proximations. In Proceedings of the 42nd Meeting of the ACL, pages 320-327, Barcelona, Spain.",
                "links": null
            },
            "BIBREF5": {
                "ref_id": "b5",
                "title": "Parser evaluation: a survey and a new proposal",
                "authors": [
                    {
                        "first": "John",
                        "middle": [],
                        "last": "Carroll",
                        "suffix": ""
                    },
                    {
                        "first": "Ted",
                        "middle": [],
                        "last": "Briscoe",
                        "suffix": ""
                    },
                    {
                        "first": "Antonio",
                        "middle": [],
                        "last": "Sanfilippo",
                        "suffix": ""
                    }
                ],
                "year": 1998,
                "venue": "Proceedings of the 1st LREC Conference",
                "volume": "",
                "issue": "",
                "pages": "447--454",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "John Carroll, Ted Briscoe, and Antonio Sanfilippo. 1998. Parser evaluation: a survey and a new proposal. In Proceed- ings of the 1st LREC Conference, pages 447-454, Granada, Spain.",
                "links": null
            },
            "BIBREF6": {
                "ref_id": "b6",
                "title": "Coarse-to-fine nbest parsing and maxent discriminative reranking",
                "authors": [
                    {
                        "first": "Eugene",
                        "middle": [],
                        "last": "Charniak",
                        "suffix": ""
                    },
                    {
                        "first": "Mark",
                        "middle": [],
                        "last": "Johnson",
                        "suffix": ""
                    }
                ],
                "year": 2005,
                "venue": "Proceedings of the 43rd Annual Meeting of the ACL",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Eugene Charniak and Mark Johnson. 2005. Coarse-to-fine n- best parsing and maxent discriminative reranking. In Pro- ceedings of the 43rd Annual Meeting of the ACL, University of Michigan, Ann Arbor.",
                "links": null
            },
            "BIBREF7": {
                "ref_id": "b7",
                "title": "A maximum-entropy-inspired parser",
                "authors": [
                    {
                        "first": "Eugene",
                        "middle": [],
                        "last": "Charniak",
                        "suffix": ""
                    }
                ],
                "year": 2000,
                "venue": "Proceedings of the 1st Meeting of the NAACL",
                "volume": "",
                "issue": "",
                "pages": "132--139",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Eugene Charniak. 2000. A maximum-entropy-inspired parser. In Proceedings of the 1st Meeting of the NAACL, pages 132- 139, Seattle, WA.",
                "links": null
            },
            "BIBREF8": {
                "ref_id": "b8",
                "title": "The importance of supertagging for wide-coverage CCG parsing",
                "authors": [
                    {
                        "first": "Stephen",
                        "middle": [],
                        "last": "Clark",
                        "suffix": ""
                    },
                    {
                        "first": "James",
                        "middle": [
                            "R"
                        ],
                        "last": "Curran",
                        "suffix": ""
                    }
                ],
                "year": 2004,
                "venue": "Proceedings of COLING-04",
                "volume": "",
                "issue": "",
                "pages": "282--288",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Stephen Clark and James R. Curran. 2004a. The importance of supertagging for wide-coverage CCG parsing. In Proceed- ings of COLING-04, pages 282-288, Geneva, Switzerland.",
                "links": null
            },
            "BIBREF9": {
                "ref_id": "b9",
                "title": "Head-driven statistical models for natural language parsing",
                "authors": [
                    {
                        "first": "Stephen",
                        "middle": [],
                        "last": "Clark",
                        "suffix": ""
                    },
                    {
                        "first": "James",
                        "middle": [
                            "R"
                        ],
                        "last": "Curran",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "Proceedings of the 42nd Meeting of the ACL",
                "volume": "29",
                "issue": "",
                "pages": "589--637",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Stephen Clark and James R. Curran. 2004b. Parsing the WSJ using CCG and log-linear models. In Proceedings of the 42nd Meeting of the ACL, pages 104-111, Barcelona, Spain. Michael Collins. 2003. Head-driven statistical models for natural language parsing. Computational Linguistics, 29(4):589-637.",
                "links": null
            },
            "BIBREF10": {
                "ref_id": "b10",
                "title": "Investigating GIS and smoothing for maximum entropy taggers",
                "authors": [
                    {
                        "first": "R",
                        "middle": [],
                        "last": "James",
                        "suffix": ""
                    },
                    {
                        "first": "Stephen",
                        "middle": [],
                        "last": "Curran",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Clark",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "Proceedings of the 10th Meeting of the EACL",
                "volume": "",
                "issue": "",
                "pages": "91--98",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "James R. Curran and Stephen Clark. 2003. Investigating GIS and smoothing for maximum entropy taggers. In Proceed- ings of the 10th Meeting of the EACL, pages 91-98, Bu- dapest, Hungary.",
                "links": null
            },
            "BIBREF11": {
                "ref_id": "b11",
                "title": "Generative models for statistical parsing with Combinatory Categorial Grammar",
                "authors": [
                    {
                        "first": "Julia",
                        "middle": [],
                        "last": "Hockenmaier",
                        "suffix": ""
                    },
                    {
                        "first": "Mark",
                        "middle": [],
                        "last": "Steedman",
                        "suffix": ""
                    }
                ],
                "year": 2002,
                "venue": "Proceedings of the 40th Meeting of the ACL",
                "volume": "",
                "issue": "",
                "pages": "335--342",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Julia Hockenmaier and Mark Steedman. 2002. Generative models for statistical parsing with Combinatory Categorial Grammar. In Proceedings of the 40th Meeting of the ACL, pages 335-342, Philadelphia, PA.",
                "links": null
            },
            "BIBREF12": {
                "ref_id": "b12",
                "title": "Data and Models for Statistical Parsing with Combinatory Categorial Grammar",
                "authors": [
                    {
                        "first": "Julia",
                        "middle": [],
                        "last": "Hockenmaier",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Julia Hockenmaier. 2003. Data and Models for Statistical Parsing with Combinatory Categorial Grammar. Ph.D. the- sis, University of Edinburgh.",
                "links": null
            },
            "BIBREF13": {
                "ref_id": "b13",
                "title": "Speed and accuracy in shallow and deep stochastic parsing",
                "authors": [
                    {
                        "first": "Ron",
                        "middle": [],
                        "last": "Kaplan",
                        "suffix": ""
                    },
                    {
                        "first": "Stefan",
                        "middle": [],
                        "last": "Riezler",
                        "suffix": ""
                    },
                    {
                        "first": "Tracy",
                        "middle": [
                            "H"
                        ],
                        "last": "King",
                        "suffix": ""
                    },
                    {
                        "first": "John",
                        "middle": [
                            "T"
                        ],
                        "last": "Maxwell",
                        "suffix": ""
                    },
                    {
                        "first": "Iii",
                        "middle": [],
                        "last": "",
                        "suffix": ""
                    },
                    {
                        "first": "Alexander",
                        "middle": [],
                        "last": "Vasserman",
                        "suffix": ""
                    },
                    {
                        "first": "Richard",
                        "middle": [],
                        "last": "Crouch",
                        "suffix": ""
                    }
                ],
                "year": 2004,
                "venue": "Proceedings of the HLT Conference and the 4th NAACL Meeting (HLT-NAACL'04)",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Ron Kaplan, Stefan Riezler, Tracy H. King, John T. Maxwell III, Alexander Vasserman, and Richard Crouch. 2004. Speed and accuracy in shallow and deep stochastic parsing. In Proceedings of the HLT Conference and the 4th NAACL Meeting (HLT-NAACL'04), Boston, MA.",
                "links": null
            },
            "BIBREF14": {
                "ref_id": "b14",
                "title": "The PARC 700 Dependency Bank",
                "authors": [
                    {
                        "first": "Tracy",
                        "middle": [
                            "H"
                        ],
                        "last": "King",
                        "suffix": ""
                    },
                    {
                        "first": "Richard",
                        "middle": [],
                        "last": "Crouch",
                        "suffix": ""
                    },
                    {
                        "first": "Stefan",
                        "middle": [],
                        "last": "Riezler",
                        "suffix": ""
                    },
                    {
                        "first": "Mary",
                        "middle": [],
                        "last": "Dalrymple",
                        "suffix": ""
                    },
                    {
                        "first": "Ronald",
                        "middle": [
                            "M"
                        ],
                        "last": "Kaplan",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "Proceedings of the LINC-03 Workshop",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Tracy H. King, Richard Crouch, Stefan Riezler, Mary Dalrym- ple, and Ronald M. Kaplan. 2003. The PARC 700 Depen- dency Bank. In Proceedings of the LINC-03 Workshop, Bu- dapest, Hungary.",
                "links": null
            },
            "BIBREF15": {
                "ref_id": "b15",
                "title": "Wide coverage parsing with stochastic attribute value grammars",
                "authors": [
                    {
                        "first": "Robert",
                        "middle": [],
                        "last": "Malouf",
                        "suffix": ""
                    },
                    {
                        "first": "",
                        "middle": [],
                        "last": "Gertjan Van Noord",
                        "suffix": ""
                    }
                ],
                "year": 2004,
                "venue": "Proceedings of the IJCNLP-04 Workshop: Beyond shallow analyses -Formalisms and statistical modeling for deep analyses",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Robert Malouf and Gertjan van Noord. 2004. Wide coverage parsing with stochastic attribute value grammars. In Pro- ceedings of the IJCNLP-04 Workshop: Beyond shallow anal- yses -Formalisms and statistical modeling for deep analyses, Hainan Island, China.",
                "links": null
            },
            "BIBREF16": {
                "ref_id": "b16",
                "title": "Deterministic dependency parsing of English text",
                "authors": [
                    {
                        "first": "Joakim",
                        "middle": [],
                        "last": "Nivre",
                        "suffix": ""
                    },
                    {
                        "first": "Mario",
                        "middle": [],
                        "last": "Scholz",
                        "suffix": ""
                    }
                ],
                "year": 2004,
                "venue": "Proceedings of COLING-2004",
                "volume": "",
                "issue": "",
                "pages": "64--70",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Joakim Nivre and Mario Scholz. 2004. Deterministic depen- dency parsing of English text. In Proceedings of COLING- 2004, pages 64-70, Geneva, Switzerland.",
                "links": null
            },
            "BIBREF17": {
                "ref_id": "b17",
                "title": "Using grammatical relations to compare parsers",
                "authors": [
                    {
                        "first": "Judita",
                        "middle": [],
                        "last": "Preiss",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "Proceedings of the 10th Meeting of the EACL",
                "volume": "",
                "issue": "",
                "pages": "291--298",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Judita Preiss. 2003. Using grammatical relations to compare parsers. In Proceedings of the 10th Meeting of the EACL, pages 291-298, Budapest, Hungary.",
                "links": null
            },
            "BIBREF18": {
                "ref_id": "b18",
                "title": "Tree-adjoining grammars and its application to statistical parsing",
                "authors": [
                    {
                        "first": "Anoop",
                        "middle": [],
                        "last": "Sarkar",
                        "suffix": ""
                    },
                    {
                        "first": "Aravind",
                        "middle": [],
                        "last": "Joshi",
                        "suffix": ""
                    }
                ],
                "year": 2003,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Anoop Sarkar and Aravind Joshi. 2003. Tree-adjoining gram- mars and its application to statistical parsing. In Rens Bod, Remko Scha, and Khalil Sima'an, editors, Data-oriented parsing. CSLI.",
                "links": null
            },
            "BIBREF19": {
                "ref_id": "b19",
                "title": "The Syntactic Process",
                "authors": [
                    {
                        "first": "Mark",
                        "middle": [],
                        "last": "Steedman",
                        "suffix": ""
                    }
                ],
                "year": 2000,
                "venue": "",
                "volume": "",
                "issue": "",
                "pages": "",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Mark Steedman. 2000. The Syntactic Process. The MIT Press, Cambridge, MA.",
                "links": null
            },
            "BIBREF20": {
                "ref_id": "b20",
                "title": "Parse disambiguation for a rich HPSG grammar",
                "authors": [
                    {
                        "first": "Kristina",
                        "middle": [],
                        "last": "Toutanova",
                        "suffix": ""
                    },
                    {
                        "first": "Christopher",
                        "middle": [],
                        "last": "Manning",
                        "suffix": ""
                    },
                    {
                        "first": "Stuart",
                        "middle": [],
                        "last": "Shieber",
                        "suffix": ""
                    },
                    {
                        "first": "Dan",
                        "middle": [],
                        "last": "Flickinger",
                        "suffix": ""
                    },
                    {
                        "first": "Stephan",
                        "middle": [],
                        "last": "Oepen",
                        "suffix": ""
                    }
                ],
                "year": 2002,
                "venue": "Proceedings of the First Workshop on Treebanks and Linguistic Theories",
                "volume": "",
                "issue": "",
                "pages": "253--263",
                "other_ids": {},
                "num": null,
                "urls": [],
                "raw_text": "Kristina Toutanova, Christopher Manning, Stuart Shieber, Dan Flickinger, and Stephan Oepen. 2002. Parse disambiguation for a rich HPSG grammar. In Proceedings of the First Work- shop on Treebanks and Linguistic Theories, pages 253-263, Sozopol, Bulgaria.",
                "links": null
            }
        },
        "ref_entries": {
            "FIGREF0": {
                "text": "S [dcl ]\\NP 1 )/(S [to]\\NP ) 2 , which is associated with wants; this combines with (S [to]\\NP )/(S [b]\\NP ), associated with to; and the argument of (S [to]\\NP )/(S [b]\\NP ) is wean.",
                "num": null,
                "type_str": "figure",
                "uris": null
            },
            "TABREF0": {
                "text": "The first stage was to create a mapping between the CCG dependencies and the 250",
                "num": null,
                "html": null,
                "content": "<table><tr><td/><td>description</td></tr><tr><td>conj</td><td>coordinator</td></tr><tr><td>aux</td><td>auxiliary</td></tr><tr><td>det</td><td>determiner</td></tr><tr><td>ncmod</td><td>non-clausal modifier</td></tr><tr><td>xmod</td><td>unsaturated predicative modifier</td></tr><tr><td>cmod</td><td>saturated clausal modifier</td></tr><tr><td>pmod</td><td>PP modifier with a PP complement</td></tr><tr><td colspan=\"2\">ncsubj non-clausal subject</td></tr><tr><td>xsubj</td><td>unsaturated predicative subject</td></tr><tr><td>csubj</td><td>saturated clausal subject</td></tr><tr><td>dobj</td><td>direct object</td></tr><tr><td>obj2</td><td>second object</td></tr><tr><td>iobj</td><td>indirect object</td></tr><tr><td>pcomp</td><td>PP which is a PP complement</td></tr><tr><td>xcomp</td><td>unsaturated VP complement</td></tr><tr><td>ccomp</td><td>saturated clausal complement</td></tr><tr><td>ta</td><td>textual adjunct delimited by punctuation</td></tr></table>",
                "type_str": "table"
            },
            "TABREF1": {
                "text": "GRs in B&C's annotation of DepBank",
                "num": null,
                "html": null,
                "content": "<table/>",
                "type_str": "table"
            },
            "TABREF2": {
                "text": "gives some examples of the mapping; %l indicates the word associated with the lexical category",
                "num": null,
                "html": null,
                "content": "<table><tr><td>CCG lexical category</td><td>slot GR</td></tr></table>",
                "type_str": "table"
            },
            "TABREF3": {
                "text": "Examples of the dependency mapping and %f is the head of the constituent filling the argument slot. Note that the order of %l and %f varies ac-",
                "num": null,
                "html": null,
                "content": "<table/>",
                "type_str": "table"
            },
            "TABREF4": {
                "text": "The future depends on building ties (S [dcl ]\\NP1 )/(S [to]\\NP )2 2 (xcomp %f %l %k) cat=(S [to]\\NP )/(S [b]\\NP ) wants to wean itself away from",
                "num": null,
                "html": null,
                "content": "<table><tr><td>CCG lexical category</td><td>slot GR</td><td/><td>constraint</td><td>example</td></tr><tr><td>(S [dcl ]\\NP1 )/NP2</td><td>2 (xcomp</td><td colspan=\"2\">%l %f) word=be</td><td>The parent is Imperial</td></tr><tr><td/><td colspan=\"2\">(dobj %l %f)</td><td/><td>The parent sold Imperial</td></tr><tr><td>(S [dcl ]\\NP1 )/PP2</td><td colspan=\"2\">2 (iobj %l %f)</td><td>cat=PP /NP</td><td>The loss stems from several factors</td></tr><tr><td/><td>(xcomp</td><td colspan=\"2\">%l %f) cat=PP /(S [ng]\\NP )</td></tr></table>",
                "type_str": "table"
            },
            "TABREF5": {
                "text": "91.00 92.15 94.20 89.25 91.66 96.47 90.33 93.30 400 conj 72.39 72.27 72.33 79.73 77.98 78.84 83.07 80.27 81.65 595 ta 42.61 51.37 46.58 52.31 11.64 19.05 62.07 12.59 20.93 292 det 87.73 90.48 89.09 95.25 95.42 95.34 97.27 94.09 95.",
                "num": null,
                "html": null,
                "content": "<table><tr><td/><td/><td>RASP</td><td/><td/><td>CCG parser</td><td/><td/><td>CCGbank</td><td/></tr><tr><td>Relation</td><td>Prec</td><td>Rec</td><td>F</td><td>Prec</td><td>Rec</td><td>F</td><td>Prec</td><td>Rec</td><td colspan=\"2\">F # GRs</td></tr><tr><td>aux</td><td colspan=\"9\">93.33 66</td><td>1 114</td></tr><tr><td>ncmod</td><td colspan=\"9\">75.72 69.94 72.72 75.75 79.27 77.47 78.88 80.64 79.75</td><td>3 550</td></tr><tr><td>xmod</td><td colspan=\"9\">53.21 46.63 49.70 43.46 52.25 47.45 56.54 60.67 58.54</td><td>178</td></tr><tr><td>cmod</td><td colspan=\"9\">45.95 30.36 36.56 51.50 61.31 55.98 64.77 69.09 66.86</td><td>168</td></tr><tr><td>pmod</td><td colspan=\"3\">30.77 33.33 32.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>12</td></tr><tr><td>ncsubj</td><td colspan=\"9\">79.16 67.06 72.61 83.92 75.92 79.72 88.86 78.51 83.37</td><td>1 354</td></tr><tr><td>xsubj</td><td colspan=\"3\">33.33 28.57 30.77</td><td>0.00</td><td>0.00</td><td colspan=\"4\">0.00 50.00 28.57 36.36</td><td>7</td></tr><tr><td>csubj</td><td colspan=\"3\">12.50 50.00 20.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>2</td></tr><tr><td>dobj</td><td colspan=\"9\">83.63 79.08 81.29 87.03 89.40 88.20 92.11 90.32 91.21</td><td>1 764</td></tr><tr><td>obj2</td><td colspan=\"9\">23.08 30.00 26.09 65.00 65.00 65.00 66.67 60.00 63.16</td><td>20</td></tr><tr><td>iobj</td><td colspan=\"9\">70.77 76.10 73.34 77.60 70.04 73.62 83.59 69.81 76.08</td><td>544</td></tr><tr><td>xcomp</td><td colspan=\"9\">76.88 77.69 77.28 76.68 77.69 77.18 80.00 78.49 79.24</td><td>381</td></tr><tr><td>ccomp</td><td colspan=\"9\">46.44 69.42 55.55 79.55 72.16 75.68 80.81 76.31 78.49</td><td>291</td></tr><tr><td>pcomp</td><td colspan=\"3\">72.73 66.67 69.57</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>24</td></tr><tr><td colspan=\"10\">macroaverage 62.12 63.77 62.94 65.61 63.28 64.43 71.73 65.85 68.67</td></tr><tr><td colspan=\"10\">microaverage 77.66 74.98 76.29 82.44 81.28 81.86 86.86 82.75 84.76</td></tr></table>",
                "type_str": "table"
            }
        }
    }
}