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Attaching Multiple Prepositional Phrases: Generalized Backed-off Estimation

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Paola Merlo (U. of Pennsylvania, University of Geneva) Matthew Crocker (University of Edinburgh) Cathy Berthouzoz (University of Geneva)

cmp-lg · Submitted 16 Oct 1997

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We present and algorithm which solves this problem through re-use of the relatively rich data obtained from first PP training, in resolving subsequent PP attachments.

how it is stated · asserted 1.00 read the record →

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arXiv IDcmp-lg/9710005 (version 1)
Pith IDHVSPKQ5X, Pith's permanent record of this paper and its review
CitationPaola Merlo (U. of Pennsylvania, University of Geneva) Matthew Crocker (University of Edinburgh) Cathy Berthouzoz (University of Geneva). "Attaching Multiple Prepositional Phrases: Generalized Backed-off Estimation." arXiv:cmp-lg/9710005 (1997). https://pith.science/paper/HVSPKQ5X
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,288 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.52 · states limitation 0.24 · human participants 0.02 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.14 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

There has recently been considerable interest in the use of lexically-based statistical techniques to resolve prepositional phrase attachments. To our knowledge, however, these investigations have only considered the problem of attaching the first PP, i.e., in a [V NP PP] configuration. In this paper, we consider one technique which has been successfully applied to this problem, backed-off estimation, and demonstrate how it can be extended to deal with the problem of multiple PP attachment. The multiple PP attachment introduces two related problems: sparser data (since multiple PPs are naturally rarer), and greater syntactic ambiguity (more attachment configurations which must be distinguished). We present and algorithm which solves this problem through re-use of the relatively rich data obtained from first PP training, in resolving subsequent PP attachments.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 There has recently been considerable interest in the use of lexically-based statistical techniques to resolve prepositional phrase attachments. background 1.00 0.04 0.03 asserted 1.00
s1 To our knowledge, however, these investigations have only considered the problem of attaching the first PP, i.e., in a [V NP PP] configuration. gap 0.99 0.16 0.03 asserted 0.92
s2 In this paper, we consider one technique which has been successfully applied to this problem, backed-off estimation, and demonstrate how it can be extended to deal with the problem of multiple PP attachment. contribution 0.99 0.81 0.27 asserted 1.00
s3 The multiple PP attachment introduces two related problems: sparser data (since multiple PPs are naturally rarer), and greater syntactic ambiguity (more attachment configurations which must be distinguished). background 0.89 0.23 0.05 asserted 1.00
s4 We present and algorithm which solves this problem through re-use of the relatively rich data obtained from first PP training, in resolving subsequent PP attachments. method 0.57 0.93 0.63 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from argument 0.42.

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  • full text
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  • references and citing sentences
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  • S sections
  • T body sentences
  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

There has recently been considerable interest in the use of lexically-based statistical techniques to resolve prepositional phrase attachments. To our knowledge, however, these investigations have only considered the problem of attaching the first PP, i.e., in a [V NP PP] configuration. In this paper, we consider one technique which has been successfully applied to this problem, backed-off estimation, and demonstrate how it can be extended to deal with the problem of multiple PP attachment. The multiple PP attachment introduces two related problems: sparser data (since multiple PPs are naturally rarer), and greater syntactic ambiguity (more attachment configurations which must be distinguished). We present and algorithm which solves this problem through re-use of the relatively rich data obtained from first PP training, in resolving subsequent PP attachments.

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