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Separating the articles of authors with the same name

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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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Jose M. Soler

Digital Libraries · Submitted 1 Aug 2006

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

The authors' main claim The method has proven very useful in generating groups of papers that are then selected manually.

how it is stated · asserted 1.00 read the record →

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arXiv IDcs/0608004 (version 1)
Pith IDQ4SBGCCW, Pith's permanent record of this paper and its review
CitationJose M. Soler. "Separating the articles of authors with the same name." arXiv:cs/0608004 (2006). https://pith.science/paper/Q4SBGCCW
Claim paper

The compiled record

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.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,145 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.47 · human participants 0.07 · controlled comparison 0.05 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.06 · mentions code or data availability 0.03.

The abstract, sentence by sentence

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

I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publication lists are not available.

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 I describe a method to separate the articles of different authors with the same name. contribution 0.99 0.83 0.25 asserted 0.99
s1 It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. method 1.00 0.67 0.07 asserted 1.00
s2 Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. method 0.99 0.63 0.27 hedged possibility 0.52
s3 The method has proven very useful in generating groups of papers that are then selected manually. result 0.85 0.84 0.22 asserted 1.00
s4 This simplifies considerably citation analysis when the author publication lists are not available. interpretation 0.94 0.42 0.25 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.56.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

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

I describe a method to separate the articles of different authors with the same name. It is based on a distance between any two publications, defined in terms of the probability that they would have as many coincidences if they were drawn at random from all published documents. Articles with a given author name are then clustered according to their distance, so that all articles in a cluster belong very likely to the same author. The method has proven very useful in generating groups of papers that are then selected manually. This simplifies considerably citation analysis when the author publication lists are not available.

Human review

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