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Proceedings of the Second Italian Conference on Computational Linguistics CLiC-it 2015

 | 
Cristina Bosco
, 
Sara Tonelli
, 
Fabio Massimo Zanzotto

On Mining Citations to Primary and Secondary Sources in Historiography

Giovanni Colavizza et Frédéric Kaplan

Résumé

We present preliminary results from the Linked Books project, which aims at analysing citations from the historiography on Venice. A preliminary goal is to extract and parse citations from any location in the text, especially footnotes, both to primary and secondary sources. We detail a pipeline for these tasks based on a set of classifiers, and test it on the Archivio Veneto, a journal in the domain.

Presentiamo i primi risultati del progetto Linked Books, per l’analisi delle citazioni della storiografia su Venezia. Ci prefiggiamo l’estrazione e l’analisi delle citazioni da ogni posizione nei testi, specialmente note a pi pagina, sia a fonti primarie che secondarie. Discutiamo una serie di classificatori con questo obiettivo, valutandone i risultati su Archivio Veneto, una rivista del settore.

Texte intégral

The project is funded by the Swiss National Fund under Division II, project number 205121 159961.

1. Introduction

1The Linked Books project is part of the Venice Time Machine1, a joint effort to digitise and study the history of Venice by digital means. The project goal is to analyse the history of Venice through the lens of citations, by network analytic methods. Such research is interesting because it could unlock the potential of the rich semantics of the use of citations in humanities. A preliminary step is the extraction and normalization of citations, which is a challenge in itself. In this paper we present the first results on this last topic, over a corpus of journals and monographs on the history of Venice, digitised in partnership with the Ca’ Foscari Humanities Library and the Marciana Library.

2Our contribution is three-fold. First, we address the problem of extracting citations in historiography, something rarely attempted before. Secondly, we extract citation from footnotes, with plain text as input. Lastly, we deal at the same time with two different kind of citations: to primary and to secondary sources. A primary source is a documentary evidence used to support a claim, a secondary source is a scholarly publication (Wiberley Jr, 2010). In order to solve this problem, we propose a pipeline of classifiers dealing with citation detection, extraction and parsing.

3The paper is organised as follows: a state of the art in Section 2 is followed by a methodological section explaining the pipeline and applied computational tools. A section on experiments follows, conclusions and future steps close the paper.

2. Related work

4Sciences have largely used quantitative citation data to study their practices, whilst humanities remained largely outside of the process (Ardanuy, 2013). Difficulties of a concrete nature along with peculiar features of humanistic discourse make the task not trivial.

5The lack of citation data for the humanities is well recognised, both for monographs and other kind of secondary literature (Heinzkill, 1980; Larivière et al., 2006; Linmans, 2009; Hammarfelt, 2011; Sula and Miller, 2014). Furthermore, citations are deployed within humanities in multifaceted ways, posing further challenges to their extraction and understanding (Grafton, 1999; Hellqvist, 2009; Sula and Miller, 2014).

6One core element of citations in humanities, and especially so History, is the distinction between primary and secondary sources, and the quantitative and qualitative importance of both (Frost, 1979; Hellqvist, 2009). Little previous work on the use of primary sources via citations exist, with few exceptions in the domains of biblical studies and Classics (Murai and Tokosumi, 2008; Romanello, 2014).

7The literature on citation extraction mirrors this scenario. As far as the citations to secondary sources are concerned, the development of automatic citation indexing systems has been a well explored area of research over the last two decades, starting from the seminal work of Giles et al. (1998). Increasingly, researchers are also tackling the problem of locating citations within the structure of documents (Lopez, 2009; Kim et al., 2012b; Heckmann et al., 2014). The extraction of citations to primary sources is instead a largely unexplored area, where recent effort has been produced within the fields of Classics (Romanello et al., 2009; Romanello, 2013; Romanello, 2014) and law (Francesconi et al., 2010; Galibert et al., 2010).

3. Approach

8We propose a three-staged incremental pipeline including the following steps:

  1. Text block detection of contiguous lines of text likely to contain citations, usually footnotes. The motivation for this preliminary step, inspired by Kim et al. (2012b), is to individuate the footnote space of a publication, as footnotes can span multiple pages.

  2. Citation extraction within their boundaries over one or more contiguous text lines. This stage entails a token by token classification. A further sub-step is the classification of a citation as being Primary or Secondary, meaning to primary or secondary sources respectively.

  3. Citation parsing, token by token, to detect all relevant components over a set of 50 mutually exclusive classes (e.g. Author, Title and PublicationDate for citations to secondary sources, or Archive, Fond and Series for primary sources).

  • 2 Using Python sklearn package.

9The first step is dealt with using a SVM classifier,2initially trained with a small set of morphological features.

10The second and last steps are approached with a group of CRF classifiers trained over a rich set of features, considering a bi-gram and tri-gram context, both backwards and forward. We train the models with Stochastic Gradient Descent and L2 regularisation, using the CRFSuite and default parameters (Okazaki, 2007).

11Conditional Random Fields and Supporting Vector Machines are state-of-the-art models in the field of citation extraction since the work of Peng and McCallum (2006), and were introduced first by Cortes and Vapnik (1995) and Lafferty et al. (2001) respectively.

4. Experiments

12The corpus is first digitised,3then OCRed using a commercial product with no extra training.4Our tests are based on an annotated sample of pages from the Archivio Veneto—a scholarly journal in Italian specialised in the History of Venice—randomly selected from a corpus of 92 issues from the year 1969 to 2013. The sample consists of 1138 annotated pages, for a total of 6257 annotated citations. Proper evaluation of the OCR quality and inter-annotator agreement are still pending at this stage. The annotation phase has been carried out with Brat.5No text format features—i.e. italics or type module—are used for the moment, and will be considered in a subsequent phase of the project.

4.1 Text block detection

  • 6 Frequencies are always assessed character by character.

13The first classification step is a boolean one, where we are interested in knowing if a line of text, or a group of contiguous lines, is likely to contain citations, therefore likely to be a footnote. Text blocks are defined as groups of k contiguous lines of text. This step is required by the nature of footnotes, which can span over multiple pages demanding their proper identification in order to define the input space for subsequent stages in the pipeline. For each block we extract the following features: 1- General: line number (to detect footnotes); 2- Morphological6: punctuation frequency, frequency of digits, frequency of uppercase and lower-case characters, number of white spaces, number of characters, frequency of abbreviations according to multiple patterns, average word length, average frequency of specific punctuation symbols (“.”, “,”, “(”, “)”, “[”, “]”); 3- Boolean: if the chunk begins with a possible acronym or with a digit. After experimental tuning, we settle for a poly-linear model of degree 2 over a set of alternatives (degrees 1 to 10), which has the added value of maximizing recall, the most important metric at this early stage. The best division into text-blocks is found to be with k = 2. The evaluation of this step, based on a randomlyselected third of the annotated data (3633 blocks, 2204 negative and 1429 positive), is reported in Table 1.

Table 1: Evaluation results for Text block detection

Task

Precision

Recall

F1-score

no-citation

0.96

0.95

0.96

citation

0.92

0.95

0.93

avg / total

0.95

0.95

0.95

14Our results compare with others applying similar filtering methods (Kim et al., 2012a). In the future we will test a confidence classification with threshold lower than 0.5, as to further improve recall over precision.

4.2 Citation extraction

  • 7 The full list of features is available upon request and partially inspired by Okazaki (2007).

15Given a text block likely to contain citations, we address the problem of citation extraction, meaning tokenizing the block and tagging each token as being part of a citation or not. For this phase and the next, text blocks are merged as to avoid any input being considered twice or more in the training and test sets. We merge together contiguous text lines likely to contain a citation, and consider k extra context (lines of text without citations) before and after. The set of features used for this step is organised in the following classes:7

  1. Shape of the token: according to each character being upper-case, lower-case or punctuation. E.g. ”UUU.” for a token of length 4 with 3 upper-case characters and a final dot.

  2. Type of the token: according to a set of classes such as if the token is a digit, or made of all upper-case letters, etc.

  3. Boolean features: if the token is a 2 or 4 digit number, if it contains digits, if it contains upper or lower case characters, etc.

  4. Other features: the token itself and it’s position in the current line.

  5. A more limited set of features is also considered in a bi and tri-gram conditioning over a sliding window within the preceding and following 3 tokens, namely: the tokens themselves, their shape and type, their position in the line.

16The evaluation was conducted on a set of 19852 tokens (5240 primary and 14612 secondary) and 1056 text blocks, corresponding to a random third of the annotated corpus. The most balanced context turned out to be k=2, results in Table 2. The performance is acceptably high in terms of overall item accuracy (0.95). In general, a higher context k means trading off precision for recall. Instance accuracy is apparently much lower (0.504), we must however remember that an instance at this level is a text block, possibly containing several non contiguous citations. Instance accuracy at the citation level improves to 0.78, and 0.84 if we tolerate for 1 token of difference between the golden standard and automatic tagging of a citation. We therefore attain results comparable to those Lopez (2010) got for the task of individuating non-patent references in patent text bodies.

Table 2: Evaluation results for Citation extraction

Task

Precision

Recall

F1-score

no-citation

0.978

0.917

0.947

citation

0.926

0.98

0.953

avg / total

0.952

0.949

0.95

17We further explored if a classifier trained with the same features could properly distinguish citations to primary and secondary sources. For this task each citation is parsed independently, assuming proper segmentation from the previous step. We attain an overall item accuracy of 0.967 and instance accuracy of 0.928 over the same training and testing sets. The fact that this classifier performs well allows us to consider the macrocategory (primary or secondary) as a feature in the parsing step. Results in Table 3.

4.3 Citation parsing

18This step involves the parsing of an extracted citation in order to individuate its components. The same set of features as before is used for each token, with the addition of:

  • Enhanced boolean features: if the token is a time span (e.g. “1600-1700”), if it might be a Roman number, or an abbreviation.

Table 3: Evaluation results for Primary and Secondary Citation classification

Task

Precision

Recall

F1-score

primary

0.968

0.904

0.935

secondary

0.966

0.989

0.978

avg / total

0.967

0.947

0.956

  • The macro-category (primary or secondary), as an indicator of the typology of the citation.

Table 4: Evaluation results for Citation parsing: without macro-category feature

Task

Precision

Recall

F1-score

Author

0.939

0.958

0.948

Title

0.873

0.989

0.928

Pub.Place

0.927

0.899

0.913

Pub.Year

0.927

0.861

0.893

Pagination

0.961

0.978

0.969

Archive

0.968

0.912

0.939

ArchivalRef.

0.909

0.884

0.896

Folder

0.955

0.938

0.947

Registry

0.957

0.901

0.928

Cartulation

0.938

0.908

0.921

Foliation

0.862

0.890

0.875

 

Table 5: Evaluation results for Citation parsing: with macro-category feature

Task

Precision

Recall

F1-score

Author

0.94

0.957

0.948

Title

0.9

0.984

0.94

Pub.Place

0.931

0.908

0.919

Pub.Year

0.945

0.893

0.918

Pagination

0.953

0.984

0.968

Archive

0.969

0.919

0.943

ArchivalRef.

0.901

0.895

0.898

Folder

0.956

0.942

0.949

Registry

0.971

0.901

0.935

Cartulation

0.964

0.934

0.949

Foliation

0.892

0.884

0.888

  • 8 The full list of results is available upon request.

19We test over a random 30% of the corpus and report only results of parsing with no extra context, which predictably gave the best results. Overall item and instance accuracy are 0.884 and 0.575 without the macro-category feature, and 0.893 and 0.592 with it. The testing set is comparable and proportional in size, yet different in sampling to the one used in step 2. Results in Table 4 and Table 5 only report the most significant classes in order to understand a citation, for citations secondary (above) and primary sources (below) respectively.8

20The macro-category has only a marginal, albeit positive impact. Furthermore, some categories are either under-represented in terms of training instances, or easily mistaken for another one, contributing to the overall degradation of results. Such is the case for Editor or Curator, frequently classified as Author. In general several categories could be grouped, and lookup features—over list of names or library catalogues—should greatly improve our results.

21The model performs well for the most significant categories, in comparison to models trained on more data and/or fewer categories and/or on references and not footnote citations. Specifically, we improve on Lopez (2010), Kim et al. (2012b), Romanello (2013), and compare to Heckmann et al. (2014).

5. Conclusions and future work

22We presented a pipeline for recognizing and parsing citations to primary and secondary sources from historiography on Venice, with a case study on the Archivio Veneto journal. A first filtering step allows us to detect text blocks likely to contain citations, usually footnotes, by a SVM classifier trained on a simple set of morphological features. We then detect citation boundaries and macro-categories (to primary and secondary sources) using more rich features and CRFs. The last step in our pipeline is the fine-grained parsing of each extracted citation, in order to prepare them for further processing and analysis.

23In the future we plan to design more advanced feature sets, first of all considering text format features. Secondly, we will implement the next package of our chain: an error-tolerant normalizer which will uniform all citations to the same primary or secondary source within a publication, as a means to minimise the impact of classification errors during previous steps.

24We thank Maud Ehrmann and Jean-Cédric Chappelier, EPFL, for useful comments.

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Notes

1 http://vtm.epfl.ch/.

2 Using Python sklearn package.

3 With 4DigitalBooks DLmini scanners.

4 Abbyy FineReader Corporate 12.

5 http://brat.nlplab.org/.

6 Frequencies are always assessed character by character.

7 The full list of features is available upon request and partially inspired by Okazaki (2007).

8 The full list of results is available upon request.

Auteurs

EPFL, CDH, DH Laboratory, Lausanne, Switzerland - giovanni.colavizza@epfl.ch

EPFL, CDH, DH Laboratory, Lausanne, Switzerland - frederic.kaplan@epfl.ch

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