Abstract
We present a text mining environment that supports entity-centric mining of terascale historical newspaper collections. Information about entities and their relation to each other is often crucial for historical research. However, most text mining tools provide only very basic support for dealing with entities, typically at most including facilities for entity tagging. Historians, on the other hand, are typically interested in the relations between entities and the contexts in which these are mentioned. In this paper, we focus on person entities. We provide an overview of the tool and describe how person-centric mining can be integrated in a general-purpose text mining environment. We also discuss our approach for automatically extracting person networks from newspaper archives, which includes a novel method for person name disambiguation, which is particularly suited for the newspaper domain and obtains state-of-the-art disambiguation results.
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Notes
- 1.
The converse also applies: a given referent can be referred to by various expressions. This is dealt with by automatic coreference resolution. We do not address this aspect in the current paper.
- 2.
- 3.
We use PostgreSQL: http://www.postgresql.org/.
- 4.
Based on SOLR/Lucene: http://lucene.apache.org/solr/.
- 5.
Developed using Ruby/Rails/Angular.
- 6.
At the time of writing, the person network module has already been implemented, tested, and evaluated, but is not yet fully integrated in the main tool. Consequently, this visualization option is missing from the screenshot in Fig. 1.
- 7.
The examples are taken from the St. Vither Volkzeitung, a German newspaper based in St. Vith (Belgium). We present here the sentences in their translation into English for ease of understanding. We work with the original data.
- 8.
Note that this approach is optimized for the four languages in which we work: English, German, Dutch, and Italian. Dealing with languages with different naming conventions (such as Spanish and Chinese) would mean having to modify slightly the approach.
- 9.
This is a relatively safe simplifying assumption, reminiscent of the “one-sense-per-discourse” principle often adopted in word sense disambiguation.
- 10.
We implemented a set of heuristics to detect matching names. For example, a first name-surname combination matches with an identical surname string which does not contain a first name.
- 11.
For a more detailed description of the method, experiments, and results, see [10].
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Coll Ardanuy, M., Knauth, J., Beliankou, A., van den Bos, M., Sporleder, C. (2016). Person-Centric Mining of Historical Newspaper Collections. In: Fuhr, N., Kovács, L., Risse, T., Nejdl, W. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2016. Lecture Notes in Computer Science(), vol 9819. Springer, Cham. https://doi.org/10.1007/978-3-319-43997-6_25
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