| { |
| "paper_id": "P10-1022", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T09:21:20.797627Z" |
| }, |
| "title": "Rebanking CCGbank for improved NP interpretation", |
| "authors": [ |
| { |
| "first": "Matthew", |
| "middle": [], |
| "last": "Honnibal", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "University of Sydney NSW 2006", |
| "location": { |
| "country": "Australia" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "James", |
| "middle": [ |
| "R" |
| ], |
| "last": "Curran", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "University of Sydney NSW 2006", |
| "location": { |
| "country": "Australia" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Johan", |
| "middle": [], |
| "last": "Bos", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "University of Groningen", |
| "location": { |
| "country": "The Netherlands" |
| } |
| }, |
| "email": "bos@meaningfactory.com" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "Once released, treebanks tend to remain unchanged despite any shortcomings in their depth of linguistic analysis or coverage of specific phenomena. Instead, separate resources are created to address such problems. In this paper we show how to improve the quality of a treebank, by integrating resources and implementing improved analyses for specific constructions. We demonstrate this rebanking process by creating an updated version of CCGbank that includes the predicate-argument structure of both verbs and nouns, base-NP brackets, verb-particle constructions, and restrictive and non-restrictive nominal modifiers; and evaluate the impact of these changes on a statistical parser.", |
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| "paper_id": "P10-1022", |
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| "abstract": [ |
| { |
| "text": "Once released, treebanks tend to remain unchanged despite any shortcomings in their depth of linguistic analysis or coverage of specific phenomena. Instead, separate resources are created to address such problems. In this paper we show how to improve the quality of a treebank, by integrating resources and implementing improved analyses for specific constructions. We demonstrate this rebanking process by creating an updated version of CCGbank that includes the predicate-argument structure of both verbs and nouns, base-NP brackets, verb-particle constructions, and restrictive and non-restrictive nominal modifiers; and evaluate the impact of these changes on a statistical parser.", |
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| "section": "Abstract", |
| "sec_num": null |
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| "text": "Progress in natural language processing relies on direct comparison on shared data, discouraging improvements to the evaluation data. This means that we often spend years competing to reproduce partially incorrect annotations. It also encourages us to approach related problems as discrete tasks, when a new data set that adds deeper information establishes a new incompatible evaluation.", |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
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| "text": "Direct comparison has been central to progress in statistical parsing, but it has also caused problems. Treebanking is a difficult engineering task: coverage, cost, consistency and granularity are all competing concerns that must be balanced against each other when the annotation scheme is developed. The difficulty of the task means that we ought to view treebanking as an ongoing process akin to grammar development, such as the many years of work on the ERG (Flickinger, 2000) .", |
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| "start": 462, |
| "end": 480, |
| "text": "(Flickinger, 2000)", |
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| "section": "Introduction", |
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| "text": "This paper demonstrates how a treebank can be rebanked to incorporate novel analyses and infor-mation from existing resources. We chose to work on CCGbank (Hockenmaier and Steedman, 2007) , a Combinatory Categorial Grammar (Steedman, 2000) treebank acquired from the Penn Treebank (Marcus et al., 1993) . This work is equally applicable to the corpora described by Miyao et al. (2004) , Shen et al. (2008) or Cahill et al. (2008) .", |
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| "start": 155, |
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| "text": "(Hockenmaier and Steedman, 2007)", |
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| "start": 223, |
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| "text": "(Steedman, 2000)", |
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| "start": 281, |
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| "text": "(Marcus et al., 1993)", |
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| "start": 365, |
| "end": 384, |
| "text": "Miyao et al. (2004)", |
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| }, |
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| "start": 387, |
| "end": 405, |
| "text": "Shen et al. (2008)", |
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| "start": 409, |
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| "text": "Cahill et al. (2008)", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "Our first changes integrate four previously suggested improvements to CCGbank. We then describe a novel CCG analysis of NP predicateargument structure, which we implement using NomBank (Meyers et al., 2004) . Our analysis allows the distinction between core and peripheral arguments to be represented for predicate nouns.", |
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| "start": 185, |
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| "text": "(Meyers et al., 2004)", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "With this distinction, an entailment recognition system could recognise that Google's acquisition of YouTube entailed Google acquired YouTube, because equivalent predicate-argument structures are built for both. Our analysis also recovers nonlocal dependencies mediated by nominal predicates; for instance, Google is the agent of acquire in Google's decision to acquire YouTube.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "The rebanked corpus extends CCGbank with:", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "1. NP brackets from Vadas and Curran (2008) ; 2. Restored and normalised punctuation;", |
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| "start": 20, |
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| "text": "Vadas and Curran (2008)", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "3. Propbank-derived verb subcategorisation;", |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "4. Verb particle structure drawn from Propbank;", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "5. Restrictive and non-restrictive adnominals;", |
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| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "6. Reanalyses to promote better head-finding;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
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| { |
| "text": "7. Nombank-derived noun subcategorisation.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "Together, these changes modify 30% of the labelled dependencies in CCGbank, demonstrating how multiple resources can be brought together in a single, richly annotated corpus. We then train and evaluate a parser for these changes, to investigate their impact on the accuracy of a state-of-theart statistical CCG parser.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "Formalisms like HPSG (Pollard and Sag, 1994) , LFG (Kaplan and Bresnan, 1982) , and CCG (Steedman, 2000) are linguistically motivated in the sense that they attempt to explain and predict the limited variation found in the grammars of natural languages. They also attempt to specify how grammars construct semantic representations from surface strings, which is why they are sometimes referred to as deep grammars. Analyses produced by these formalisms can be more detailed than those produced by skeletal phrasestructure parsers, because they produce fully specified predicate-argument structures. Unfortunately, statistical parsers do not take advantage of this potential detail. Statistical parsers induce their grammars from corpora, and the corpora for linguistically motivated formalisms currently do not contain high quality predicateargument annotation, because they were derived from the Penn Treebank (PTB Marcus et al., 1993) . Manually written grammars for these formalisms, such as the ERG HPSG grammar (Flickinger, 2000) and the XLE LFG grammar (Butt et al., 2006) produce far more detailed and linguistically correct analyses than any English statistical parser, due to the comparatively coarse-grained annotation schemes of the corpora statistical parsers are trained on. While rule-based parsers use grammars that are carefully engineered (e.g. Oepen et al., 2004) , and can be updated to reflect the best linguistic analyses, statistical parsers have so far had to take what they are given.", |
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| "end": 44, |
| "text": "(Pollard and Sag, 1994)", |
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| "end": 77, |
| "text": "(Kaplan and Bresnan, 1982)", |
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| "start": 88, |
| "end": 104, |
| "text": "(Steedman, 2000)", |
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| { |
| "start": 911, |
| "end": 936, |
| "text": "(PTB Marcus et al., 1993)", |
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| "start": 1016, |
| "end": 1034, |
| "text": "(Flickinger, 2000)", |
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| "start": 1059, |
| "end": 1078, |
| "text": "(Butt et al., 2006)", |
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| }, |
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| "start": 1362, |
| "end": 1381, |
| "text": "Oepen et al., 2004)", |
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| ], |
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| "section": "Background and motivation", |
| "sec_num": "2" |
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| { |
| "text": "What we suggest in this paper is that a treebank's grammar need not last its lifetime. For a start, there have been many annotations of the PTB that add much of the extra information needed to produce very high quality analyses for a linguistically motivated grammar. There are also other transformations which can be made with no additional information. That is, sometimes the existing trees allow transformation rules to be written that improve the quality of the grammar.", |
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| "section": "Background and motivation", |
| "sec_num": "2" |
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| "text": "Linguistic theories are constantly changing, which means that there is a substantial lag between what we (think we) understand of grammar and the annotations in our corpora. The grammar engineering process we describe, which we dub rebanking, is intended to reduce this gap, tightening the feedback loop between formal and computational linguistics.", |
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| "section": "Background and motivation", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Combinatory Categorial Grammar (CCG; Steedman, 2000) is a lexicalised grammar, which means that all grammatical dependencies are specified in the lexical entries and that the production of derivations is governed by a small set of rules.", |
| "cite_spans": [], |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| { |
| "text": "Lexical categories are either atomic (S , NP , PP , N ), or a functor consisting of a result, directional slash, and argument. For instance, in might head a PP -typed constituent with one NP -typed argument, written as PP /NP .", |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| "text": "A category can have a functor as its result, so that a word can have a complex valency structure. For instance, a verb phrase is represented by the category S \\NP : it is a function from a leftward NP (a subject) to a sentence. A transitive verb requires an object to become a verb phrase, producing the category (S \\NP )/NP .", |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| { |
| "text": "A CCG grammar consists of a small number of schematic rules, called combinators. CCG extends the basic application rules of pure categorial grammar with (generalised) composition rules and type raising. The most common rules are:", |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| "text": "X /Y Y \u21d2 X (>) Y X \\Y \u21d2 X (<) X /Y Y /Z \u21d2 X /Z (>B) Y \\Z X \\Y \u21d2 X \\Z (< B) Y /Z X \\Y \u21d2 X /Z (< B \u00d7 )", |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| "text": "CCGbank (Hockenmaier and Steedman, 2007) extends this compact set of combinatory rules with a set of type-changing rules, designed to strike a better balance between sparsity in the category set and ambiguity in the grammar. We mark typechanging rules TC in our derivations.", |
| "cite_spans": [ |
| { |
| "start": 8, |
| "end": 40, |
| "text": "(Hockenmaier and Steedman, 2007)", |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| { |
| "text": "In wide-coverage descriptions, categories are generally modelled as typed-feature structures (Shieber, 1986) , rather than atomic symbols. This allows the grammar to include a notion of headedness, and to unify under-specified features.", |
| "cite_spans": [ |
| { |
| "start": 93, |
| "end": 108, |
| "text": "(Shieber, 1986)", |
| "ref_id": "BIBREF23" |
| } |
| ], |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| "text": "We occasionally must refer to these additional details, for which we employ the following notation. Features are annotated in square-brackets, e.g. S [dcl ]. Head-finding indices are annotated on categories in subscripts, e.g. (NP y \\NP y )/NP z . The index of the word the category is assigned to is left implicit. We will sometimes also annotate derivations with the heads of categories as they are being built, to help the reader keep track of what lexemes have been bound to which categories.", |
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| "section": "Combinatory Categorial Grammar", |
| "sec_num": "2.1" |
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| "text": "There have been a few papers describing corrections to CCGbank. We bring these corrections together for the first time, before building on them with our further changes.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Combining CCGbank corrections", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Compound noun phrases can nest inside each other, creating bracketing ambiguities:", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Compound noun brackets", |
| "sec_num": "3.1" |
| }, |
| { |
| "text": "(1) (crude oil) prices", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Compound noun brackets", |
| "sec_num": "3.1" |
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| { |
| "text": "(2) crude (oil prices)", |
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| "section": "Compound noun brackets", |
| "sec_num": "3.1" |
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| { |
| "text": "The structure of such compound noun phrases is left underspecified in the Penn Treebank (PTB), because the annotation procedure involved stitching together partial parses produced by the Fidditch parser (Hindle, 1983) , which produced flat brackets for these constructions. The bracketing decision was also a source of annotator disagreement (Bies et al., 1995) .", |
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| { |
| "start": 203, |
| "end": 217, |
| "text": "(Hindle, 1983)", |
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| { |
| "start": 342, |
| "end": 361, |
| "text": "(Bies et al., 1995)", |
| "ref_id": "BIBREF0" |
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| "section": "Compound noun brackets", |
| "sec_num": "3.1" |
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| "text": "When Hockenmaier and Steedman (2002) went to acquire a CCG treebank from the PTB, this posed a problem. There is no equivalent way to leave these structures under-specified in CCG, because derivations must be binary branching. They therefore employed a simple heuristic: assume all such structures branch to the right. Under this analysis, crude oil is not a constituent, producing an incorrect analysis as in (1). Vadas and Curran (2007) addressed this by manually annotating all of the ambiguous noun phrases in the PTB, and went on to use this information to correct 20,409 dependencies (1.95%) in CCGbank (Vadas and Curran, 2008) . Our changes build on this corrected corpus.", |
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| "start": 5, |
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| "text": "Hockenmaier and Steedman (2002)", |
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| "section": "Compound noun brackets", |
| "sec_num": "3.1" |
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| "text": "The syntactic analysis of punctuation is notoriously difficult, and punctuation is not always treated consistently in the Penn Treebank (Bies et al., 1995) . Hockenmaier (2003) determined that quotation marks were particularly problematic, and therefore removed them from CCGbank altogether. We use the process described by Tse and Curran (2008) to restore the quotation marks and shift commas so that they always attach to the constituent to their left. This allows a grammar rule to be removed, preventing a great deal of spurious ambiguity and improving the speed of the C&C parser (Clark and Curran, 2007) by 37%.", |
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| "start": 136, |
| "end": 155, |
| "text": "(Bies et al., 1995)", |
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| "start": 158, |
| "end": 176, |
| "text": "Hockenmaier (2003)", |
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| "start": 324, |
| "end": 345, |
| "text": "Tse and Curran (2008)", |
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| "text": "(Clark and Curran, 2007)", |
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| "section": "Punctuation corrections", |
| "sec_num": "3.2" |
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| "text": "Semantic role descriptions generally recognise a distinction between core arguments, whose role comes from a set specific to the predicate, and peripheral arguments, who have a role drawn from a small, generic set. This distinction is represented in the surface syntax in CCG, because the category of a verb must specify its argument structure. In (3) as a director is annotated as a complement; in (4) it is an adjunct:", |
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| "section": "Verb predicate-argument corrections", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "(3) He NP", |
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| "section": "Verb predicate-argument corrections", |
| "sec_num": "3.3" |
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| { |
| "text": "joined (S \\NP )/PP as a director PP (4) He NP joined S \\NP as a director (S \\NP )\\(S \\NP )", |
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| "ref_spans": [], |
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| "section": "Verb predicate-argument corrections", |
| "sec_num": "3.3" |
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| "text": "CCGbank contains noisy complement and adjunct distinctions, because they were drawn from PTB function labels which imperfectly represent the distinction. In our previous work we used Propbank (Palmer et al., 2005) to convert 1,543 complements to adjuncts and 13,256 adjuncts to complements (Honnibal and Curran, 2007) . If a constituent such as as a director received an adjunct category, but was labelled as a core argument in Propbank, we changed it to a complement, using its head's part-of-speech tag to infer its constituent type. We performed the equivalent transformation to ensure all peripheral arguments of verbs were analysed as adjuncts.", |
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| "start": 192, |
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| "text": "(Palmer et al., 2005)", |
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| "start": 290, |
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| "text": "(Honnibal and Curran, 2007)", |
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| "section": "Verb predicate-argument corrections", |
| "sec_num": "3.3" |
| }, |
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| "text": "Propbank also offers reliable annotation of verbparticle constructions. This was not available in the PTB, so Hockenmaier and Steedman (2007) annotated all intransitive prepositions as adjuncts:", |
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| "start": 110, |
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| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
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| { |
| "text": "(5) He NP woke S \\NP up (S \\NP )\\(S \\NP )", |
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| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "We follow Constable and Curran (2009) in exploiting the Propbank annotations to add verbparticle distinctions to CCGbank, by introducing a new atomic category PT for particles, and changing their status from adjuncts to complements:", |
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| "text": "Constable and Curran (2009)", |
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| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
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| { |
| "text": "(6) He NP woke (S \\NP )/PT up PT", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "This analysis could be improved by adding extra head-finding logic to the verbal category, to recognise the multi-word expression as the head. 7and 8, but the noun cannot casemark them directly, so of and the genitive clitic are pressed into service. The semantic role depends on both the predicate and subcategorisation frame: In (9), the genitive introduces the patient, but when the patient is supplied by the PP, it instead introduces the agent. The mapping differs for gift, where the genitive introduces the agent.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "(9) Carthage's p destruction Pred.", |
| "eq_num": "(10)" |
| } |
| ], |
| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "Peripheral arguments, which supply generically available modifiers of time, place, cause, quality etc, can be realised by pre-and post-modifiers:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "(13) The portrait in the Louvre 14The fine portrait (15) The Louvre's portraits These are distinct from core arguments because their interpretation does not depend on the predicate. The ambiguity can be seen in an NP such as The nobleman's portrait, where the genitive could mark possession (peripheral), or it could introduce the patient (core). The distinction between core and peripheral arguments is particularly difficult for compound nouns, as pre-modification is very productive in English.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Verb-particle constructions", |
| "sec_num": "3.4" |
| }, |
| { |
| "text": "We designed our analysis for transparency between the syntax and the predicate-argument structure, by stipulating that all and only the core arguments should be syntactic arguments of the predicate's category. This is fairly straightforward for arguments introduced by prepositions:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "destruction of Carthage N /PP y PP y /NP y NP > PP Carthage > N", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "destruction In our analysis, the head of of Carthage is Carthage, as of is assumed to be a semantically transparent case-marker. We apply this analysis to prepositional phrases that provide arguments to verbs as well -a departure from CCGbank.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "Prepositional phrases that introduce peripheral arguments are analysed as syntactic adjuncts:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "The war in 149 B.C.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "NP y /N y N (N y \\N y )/NP z NP > (N y \\N y ) in < N war > NP", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "war Adjunct prepositional phrases remain headed by the preposition, as it is the preposition's semantics that determines whether they function as temporal, causal, spatial etc. arguments. We follow Hockenmaier and Steedman (2007) in our analysis of genitives which realise peripheral arguments, such as the literal possessive:", |
| "cite_spans": [ |
| { |
| "start": 214, |
| "end": 229, |
| "text": "Steedman (2007)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "Rome s aqueducts NP (NP y /N y )\\NP z N < (NP y /N y ) s > NP", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "aqueducts Arguments introduced by possessives are a little trickier, because the genitive also functions as a determiner. We achieve this by having the noun subcategorise for the argument, which we type PP , and having the possessive subcategorise for the unsaturated noun to ultimately produce an NP : Figure 2 : The coindexing on decision's category allows the hard-to-reach agent of buy to be recovered. A non-normal form derivation is shown so that instantiated variables can be seen.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 303, |
| "end": 311, |
| "text": "Figure 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "Google s decision to buy YouTube NP (NP y /(N y /PP z ) y )\\NP z (N /PP y )/(S [to] z \\NP y ) z (S [to] y \\NP z ) y /(S [b] y \\NP z ) y (S [b]\\NP y )/NP z NP < > NP y /(N y /PP Google ) y S [b]\\NP y >B > NP decision /(S [to] y \\NP Google ) y S [to] buy \\NP y > NP", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CCG analysis", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "NP (NP y /(N y /PP z ) y )\\NP z N /PP y < (NP y /(N y /PP Carthage ) y ) s >", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Carthage s destruction", |
| "sec_num": null |
| }, |
| { |
| "text": "NP destruction In this analysis, we regard the genitive clitic as a case-marker that performs a movement operation roughly analogous to WH-extraction. Its category is therefore similar to the one used in object extraction, (N \\N )/(S /NP ). Figure 1 shows an example with multiple core arguments.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 241, |
| "end": 249, |
| "text": "Figure 1", |
| "ref_id": "FIGREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Carthage s destruction", |
| "sec_num": null |
| }, |
| { |
| "text": "This analysis allows recovery of verbal arguments of nominalised raising and control verbs, a construction which both Gildea and Hockenmaier (2003) and Boxwell and White (2008) identify as a problem case when aligning Propbank and CCGbank. Our analysis accommodates this construction effortlessly, as shown in Figure 2 . The category assigned to decision can coindex the missing NP argument of buy with its own PP argument. When that argument is supplied by the genitive, it is also supplied to the verb, buy, filling its dependency with its agent, Google. This argument would be quite difficult to recover using a shallow syntactic analysis, as the path would be quite long. There are 494 such verb arguments mediated by nominal predicates in Sections 02-21.", |
| "cite_spans": [ |
| { |
| "start": 118, |
| "end": 147, |
| "text": "Gildea and Hockenmaier (2003)", |
| "ref_id": "BIBREF8" |
| }, |
| { |
| "start": 152, |
| "end": 176, |
| "text": "Boxwell and White (2008)", |
| "ref_id": "BIBREF1" |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 310, |
| "end": 318, |
| "text": "Figure 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Carthage s destruction", |
| "sec_num": null |
| }, |
| { |
| "text": "These analyses allow us to draw complement/adjunct distinctions for nominal predicates, so that the surface syntax takes us very close to a full predicate-argument analysis. The only information we are not specifying in the syntactic analysis are the role labels assigned to each of the syntactic arguments. We could go further and express these labels in the syntax, producing categories like (N /PP {0 } y )/PP {1 } z and (N /PP {1 } y )/PP {0 } z , but we expect that this would cause sparse data problems given the limited size of the corpus. This experiment would be an interesting subject of future work.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Carthage s destruction", |
| "sec_num": null |
| }, |
| { |
| "text": "The only local core arguments that we do not annotate as syntactic complements are compound nouns, such as decision makers. We avoided these arguments because of the productivity of nounnoun compounding in English, which makes these argument structures very difficult to recover.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Carthage s destruction", |
| "sec_num": null |
| }, |
| { |
| "text": "We currently do not have an analysis that allows support verbs to supply noun arguments, so we do not recover any of the long-range dependency structures described by Meyers et al. (2004) .", |
| "cite_spans": [ |
| { |
| "start": 167, |
| "end": 187, |
| "text": "Meyers et al. (2004)", |
| "ref_id": "BIBREF17" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Carthage s destruction", |
| "sec_num": null |
| }, |
| { |
| "text": "Our analysis requires semantic role labels for each argument of the nominal predicates in the Penn Treebank -precisely what NomBank (Meyers et al., 2004) provides. We can therefore draw our distinctions using the process described in our previous work, Honnibal and Curran (2007) .", |
| "cite_spans": [ |
| { |
| "start": 132, |
| "end": 153, |
| "text": "(Meyers et al., 2004)", |
| "ref_id": "BIBREF17" |
| }, |
| { |
| "start": 253, |
| "end": 279, |
| "text": "Honnibal and Curran (2007)", |
| "ref_id": "BIBREF13" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "NomBank follows the same format as Propbank, so the procedure is exactly the same. First, we align CCGbank and the Penn Treebank, and produce a version of NomBank that refers to CCGbank nodes. We then assume that any prepositional phrase or genitive determiner annotated as a core argument in NomBank should be analysed as a complement, while peripheral arguments and adnominals that receive no semantic role label at all are analysed as adjuncts.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "We converted 34,345 adnominal prepositional phrases to complements, leaving 18,919 as adjuncts. The most common preposition converted was of, which was labelled as a core argument 99.1% of the 19,283 times it occurred as an adnominal. The most common adjunct preposition was in, which realised a peripheral argument in 59.1% of its 7,725 occurrences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "The frequent prepositions were more skewed towards core arguments. 73% of the occurrences of the 5 most frequent prepositions (of, in, for, on and to) realised peripheral arguments, compared with 53% for other prepositions.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "Core arguments were also more common than peripheral arguments for possessives. There are 20,250 possessives in the corpus, of which 75% were converted to complements. The percentage was similar for both personal pronouns (such as his) and genitive phrases (such as the boy's).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "Adnominals can have either a restrictive or a nonrestrictive (appositional) interpretation, determining the potential reference of the noun phrase it modifies. This ambiguity manifests itself in whether prepositional phrases, relative clauses and other adnominals are analysed as modifiers of either N or NP, yielding a restrictive or nonrestrictive interpretation respectively.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Adding restrictivity distinctions", |
| "sec_num": "5" |
| }, |
| { |
| "text": "In CCGbank, all adnominals attach to NP s, producing non-restrictive interpretations.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Adding restrictivity distinctions", |
| "sec_num": "5" |
| }, |
| { |
| "text": "We therefore move restrictive adnominals to N nodes:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Adding restrictivity distinctions", |
| "sec_num": "5" |
| }, |
| { |
| "text": "All staff on casual contracts NP /N N (N \\N )/NP N /N N > N TC NP > N \\N < N > NP", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Adding restrictivity distinctions", |
| "sec_num": "5" |
| }, |
| { |
| "text": "This corrects the previous interpretation, which stated that there were no permanent staff.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Adding restrictivity distinctions", |
| "sec_num": "5" |
| }, |
| { |
| "text": "The Wall Street Journal's style guide mandates that this attachment ambiguity be managed by bracketing non-restrictive relatives with commas (Martin, 2002, p. 82) , as in casual staff, who have no health insurance, support it. We thus use punctuation to make the attachment decision.", |
| "cite_spans": [ |
| { |
| "start": 141, |
| "end": 162, |
| "text": "(Martin, 2002, p. 82)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "All NP \\NP modifiers that are not preceded by punctuation were moved to the lowest N node possible and relabelled N \\N . We select the lowest (i.e. closest to leaf) N node because some adjectives, such as present or former, require scope over the qualified noun, making it safer to attach the adnominal first.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Some adnominals in CCGbank are created by the S \\NP \u2192 NP \\NP unary type-changing rule, which transforms reduced relative clauses. We introduce a S \\NP \u2192 N \\N in its place, and add a binary rule cued by punctuation to handle the relatively rare non-restrictive reduced relative clauses.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "The rebanked corpus contains 34,134 N \\N restrictive modifiers, and 9,784 non-restrictive modifiers. Most (61%) of the non-restrictive modifiers were relative clauses.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and statistics", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "True partitive constructions consist of a quantifier (16), a cardinal (17) or demonstrative (18) applied to an NP via of. There are similar constructions headed by common nouns, as in (19):", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "(16) Some of us (17) Four of our members (18) Those of us who smoke (19) A glass of wine", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "We regard the common noun partitives as headed by the initial noun, such as glass, because this noun usually controls the number agreement. We therefore analyse these cases as nouns with prepositional arguments. In (19), glass would be assigned the category N /PP .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "True partitive constructions are different, however: they are always headed by the head of the NP supplied by of. The construction is quite common, because it provides a way to quantify or apply two different determiners.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "Partitive constructions are not given special treatment in the PTB, and were analysed as noun phrases with a PP modifier in CCGbank:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "Four of our members NP (NP y \\NP y )/NP z NP y /N y N > NP members > (NP y \\NP y ) of <", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "NP Four This analysis does not yield the correct semantics, and may even hurt parser performance, because the head of the phrase is incorrectly assigned. We correct this with the following analysis, which takes the head from the NP argument of the PP:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "Four of our members NP y /PP y PP y /NP y NP y /N y N > NP members > PP members > NP members", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "The cardinal is given the category NP /PP , in analogy with the standard determiner category which is a function from a noun to a noun phrase (NP /N ). ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Reanalysing partitive constructions", |
| "sec_num": "6" |
| }, |
| { |
| "text": "We detect this construction by identifying NPs post-modified by an of PP. The NP's head must either have the POS tag CD, or be one of the following words, determined through manual inspection of Sections 02-21: another, average, both, each, another, any, anything, both, certain, each, either, enough, few, little, most, much, neither, nothing, other, part, plenty, several, some, something, that, those. Having identified the construction, we simply relabel the NP to NP /PP , and the NP \\NP adnominal to PP . We identified and reanalysed 3,010 partitive genitives in CCGbank.", |
| "cite_spans": [ |
| { |
| "start": 211, |
| "end": 404, |
| "text": "another, average, both, each, another, any, anything, both, certain, each, either, enough, few, little, most, much, neither, nothing, other, part, plenty, several, some, something, that, those.", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and Statistics", |
| "sec_num": "6.1" |
| }, |
| { |
| "text": "all,", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and Statistics", |
| "sec_num": "6.1" |
| }, |
| { |
| "text": "7 Similarity to CCGbank Table 1 shows the percentage of labelled dependencies (L. Deps), unlabelled dependencies (U. Deps) and lexical categories (Cats) that remained the same after each set of changes. A labelled dependency is a 4-tuple consisting of the head, the argument, the lexical category of the head, and the argument slot that the dependency fills. For instance, the subject fills slot 1 and the object fills slot 2 on the transitive verb category (S \\NP )/NP . There are more changes to labelled dependencies than lexical categories because one lexical category change alters all of the dependencies headed by a predicate, as they all depend on its lexical category. Unlabelled dependencies consist of only the head and argument.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 24, |
| "end": 31, |
| "text": "Table 1", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Implementation and Statistics", |
| "sec_num": "6.1" |
| }, |
| { |
| "text": "The biggest changes were those described in Sections 4 and 5. After the addition of nominal predicate-argument structure, over 50% of the labelled dependencies were changed. Many of these changes involved changing an adjunct to a complement, which affects the unlabelled dependencies because the head and argument are inverted.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Implementation and Statistics", |
| "sec_num": "6.1" |
| }, |
| { |
| "text": "Our changes make the grammar sensitive to new distinctions, which increases the number of lexical categories required. of lexical categories (Cats), the number of lexical categories that occur at least 10 times in Sections 02-21 (Cats \u2265 10), and the average number of categories available for assignment to each token in Section 00 (Cats/Word). We followed Clark and Curran's (2007) process to determine the set of categories a word could receive, which includes a part-of-speech back-off for infrequent words.", |
| "cite_spans": [ |
| { |
| "start": 357, |
| "end": 382, |
| "text": "Clark and Curran's (2007)", |
| "ref_id": "BIBREF4" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexicon statistics", |
| "sec_num": "8" |
| }, |
| { |
| "text": "The lexicon steadily grew with each set of changes, because each added information to the corpus. The addition of quotes only added two categories (LQU and RQU ), and the addition of the quote tokens slightly decreased the average categories per word. The Propbank and verb-particle changes both introduced rare categories for complicated, infrequent argument structures.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexicon statistics", |
| "sec_num": "8" |
| }, |
| { |
| "text": "The NP predicate-argument structure modifications added the most information. Head nouns were previously guaranteed the category N in CCGbank; possessive clitics always received the category (NP /N )\\NP ; and possessive personal pronouns were always NP /N . Our changes introduce new categories for these frequent tokens, which meant a substantial increase in the number of possible categories per word.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Lexicon statistics", |
| "sec_num": "8" |
| }, |
| { |
| "text": "Some of the changes we have made correct problems that have caused the performance of a statistical CCG parser to be over-estimated. Other changes introduce new distinctions, which a parser may or may not find difficult to reproduce. To investigate these issues, we trained and evaluated the C&C CCG parser on our rebanked corpora.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Parsing Evaluation", |
| "sec_num": "9" |
| }, |
| { |
| "text": "The experiments were set up as follows. We used the highest scoring configuration described by Clark and Curran (2007) , the hybrid dependency model, using gold-standard POS tags. We followed Clark and Curran in excluding sentences that could not be parsed from the evaluation. All models obtained similar coverage, between 99.0 and 99.3%. The parser was evaluated using depen- dencies generated from the gold-standard derivations (Boxwell, p.c., 2010) . Table 3 shows the accuracy of the parser on Sections 00 and 23. The parser scored slightly lower as the NP brackets, Quotes, Propbank and Particles corrections were added. This apparent decline in performance is at least partially an artefact of the evaluation. CCGbank contains some dependencies that are trivial to recover, because Hockenmaier and Steedman (2007) was forced to adopt a strictly right-branching analysis for NP brackets.", |
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| "start": 431, |
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| "text": "(Boxwell, p.c., 2010)", |
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| "text": "There was a larger drop in accuracy on the fully rebanked corpus, which included our analyses of restrictivity, partitive constructions and noun predicate-argument structure. This might also be explained by the evaluation, as the rebanked corpus includes much more fine-grained distinctions. The labelled dependencies evaluation is particularly sensitive to this, as a single category change affects multiple dependencies. This can be seen in the smaller gap in category accuracy.", |
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| "text": "We investigated whether the differences in performance were due to the different evaluation data by comparing the parsers' performance against the original parser on the dependencies they agreed upon, to allow direct comparison. To do this, we extracted the CCGbank intersection of each corpus's Section 00 dependencies. Table 4 compares the labelled and unlabelled recall of the rebanked parsers we trained against the CCGbank parser on these intersections. Note that each row refers to a different intersection, so results are not comparable between rows. This comparison shows that the declines in accuracy seen in Table 3 were largely confined to the corrected de-pendencies. The parser's performance remained fairly stable on the dependencies left unchanged.", |
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| "text": "The rebanked parser performed 0.8% worse than the CCGbank parser on the intersection dependencies, suggesting that the fine-grained distinctions we introduced did cause some sparse data problems. However, we did not change any of the parser's maximum entropy features or hyperparameters, which are tuned for CCGbank.", |
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| "text": "Research in natural language understanding is driven by the datasets that we have available. The most cited computational linguistics work to date is the Penn Treebank (Marcus et al., 1993) 1 . Propbank (Palmer et al., 2005) has also been very influential since its release, and NomBank has been used for semantic dependency parsing in the CoNLL 2008 and 2009 shared tasks.", |
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| "start": 168, |
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| }, |
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| "start": 203, |
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| "text": "(Palmer et al., 2005)", |
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| } |
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| "section": "Conclusion", |
| "sec_num": "10" |
| }, |
| { |
| "text": "This paper has described how these resources can be jointly exploited using a linguistically motivated theory of syntax and semantics. The semantic annotations provided by Propbank and Nom-Bank allowed us to build a corpus that takes much greater advantage of the semantic transparency of a deep grammar, using careful analyses and phenomenon-specific conversion rules.", |
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| "section": "Conclusion", |
| "sec_num": "10" |
| }, |
| { |
| "text": "The major areas of CCGbank's grammar left to be improved are the analysis of comparatives, and the analysis of named entities. English comparatives are diverse and difficult to analyse. Even the XTAG grammar (Doran et al., 1994) , which deals with the major constructions of English in enviable detail, does not offer a full analysis of these phenomena. Named entities are also difficult to analyse, as many entity types obey their own specific grammars. This is another example of a phenomenon that could be analysed much better in CCGbank using an existing resource, the BBN named entity corpus.", |
| "cite_spans": [ |
| { |
| "start": 208, |
| "end": 228, |
| "text": "(Doran et al., 1994)", |
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| } |
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| "ref_spans": [], |
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| "section": "Conclusion", |
| "sec_num": "10" |
| }, |
| { |
| "text": "Our rebanking has substantially improved CCGbank, by increasing the granularity and linguistic fidelity of its analyses. We achieved this by exploiting existing resources and crafting novel analyses. The process we have demonstrated can be used to train a parser that returns dependencies that abstract away as much surface syntactic variation as possible -including, now, even whether the predicate and arguments are expressed in a noun phrase or a full clause.", |
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| "sec_num": "10" |
| }, |
| { |
| "text": "http://clair.si.umich.edu/clair/anthology/rankings.cgi", |
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| "text": "James Curran was supported by Australian Research Council Discovery grant DP1097291 and the Capital Markets Cooperative Research Centre.The parsing evaluation for this paper would have been much more difficult without the assistance of Stephen Boxwell, who helped generate the gold-standard dependencies with his software.We are also grateful to the members of the CCG technicians mailing list for their help crafting the analyses, particularly Michael White, Mark Steedman and Dennis Mehay.", |
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| "section": "Acknowledgments", |
| "sec_num": null |
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