Extracting Knowledge from Parliamentary Debates for Studying Political Culture and Language

Abstract

This paper presents knowledge extraction and natural language processing methods used to enrich the knowledge graph of the plenary debates (textual transcripts of speeches) of the Parliament of Finland. This knowledge graph includes some 960 000 speeches (1907-2021) interlinked with a prosopographical knowledge graph about the politicians. A recent subset of the speeches was used to extract named entities and topical keywords for semantic searching and browsing the data and for data analysis. The process is based on linguistic analysis, named entity linking, and automatic subject indexing. The results were included into the ParliamentSampo knowledge graph in a SPARQL endpoint. This data can be used for studying parliamentary language and culture in Digital Humanities research and for developing applications, such as the ParliamentSampo portal.

Keywords

• eol>parliamentary studies
• natural language processing
• linked data
• digital humanities

1. Introduction

Parliaments enact new laws, oversee the work of the government, and decide on the state budget. Parliamentary data are used in many areas of research [ 1 ], as they provide a wealth of information on the state and functioning of democratic systems, political life and, more generally, language and culture. For these reasons, a lot of parliamentary materials have been digitized in recent decades [ 2 ]. Digitized parliamentary materials ofer a wide range of perspectives on diferent research topics and have been used in a variety of fields, such as linguistics, political science, economics, and history. A most important research material for parliament studies are the debates in the parliaments, i.e., sequences of transliterated speeches (minutes) of Members of Parliament (MP) and other politicians, through which one can study the language and its changes itself as well as the underlying societal phenomena at large [ 3 ].

This paper argues and shows that by enriching textual parliamentary speeches with linked data using knowledge extraction methods [ 4 ], it is possible to support Digital Humanities (DH) research and enhance the usability of the data in applications, such as semantic search, browsing, and data analysis. As a case study, a part the ca. 960 000 speeches of the system ParliamentSampo – Finnish Parliament on the Semantic Web [ 5, 6 ] are used. In an earlier work, the speeches covering the whole history 1907–2021 of the Parliament of Finland (PoF) were extracted from original heterogeneous data sources and transformed into a speech knowledge graph (S-KG) [ 7 ] (and also into the Parla-CLARIN format1). At the same time, the S-KG was interlinked with a prosopographical KG (P-KG) representing detailed biographical data and networks of the ca. 2800 MPs and politicians involved in the PoF activities [ 8 ]. Both graphs were published as a LOD service on the Linked Data Finland platform LDF.fi [ 9 ], including a SPARQL endpoint2. In this paper, the textual speeches of this ParliamentSampo dataset are enriched further using knowledge extraction techniques in order to support DH [ 10 ] analysis and for further development of the semantic portal ParliamentSampo on top of the endpoint.

In this paper, we first shortly overview the related work (Section 2) followed by the description of the speech data and then focus on the new data enrichments using Natural Language Processing (NLP) methods (Section 3). Section 4 discusses how the new data can be utilized in the ParliamentSampo portal. Lastly, the contributions of this work are summarized and discussed (Section 5).

2. Related Work

Regarding the digitization of parliamentary data, plenary debates have been in central role, e.g., [ 11 ] and the CLARIN list of parliamentary corpora3 in diferent countries. Parliamentary materials have also been transformed into linked data, too. A prominent example of this is the LinkedEP [ 12 ] system on the European Parliament’s data. Linked data has also been used in the Italian Parliament4, and the LinkedSaeima for the Latvian parliament [ 13 ] in addition to the Finnish ParliamentSampo system [ 5, 6 ] whose data [ 7, 8 ] was re-used in the paper.

Knowledge extraction has been applied to enrich datasets to enable distant reading approaches to studying parliamentary debates. For example, the Latvian LinkedSaeima dataset has utilized named entity linking (NEL) to enrich their metadata. Similarly, the Dutch parliamentary debates dataset [ 14 ] has been enriched with named entities (NE). The Slovenian siParl corpus [ 15 ] includes with linguistic information about the parliamentary debates in CONLL-U format.

The NLP methods used in this work have been developed mainly for handling Finnish texts. With respect to NER, some of the most relevant tools are StanfordNER [ 16 ], FiNER [ 17 ] and the FinBERT based NER tool, of which the last one is currently estimated to be the most accurate [ 18, 19, 20 ]. Similarly, there are morphological analyzers for Finnish besides the Turku Neural parser, such as the two used in this paper, Voikko and uralicNLP, the latter of which employs Omorfi [ 21]. Regarding entity linking, there are few tools available for Finnish, such as ARPA [22]. Various tools have been created also for other languages to link NEs to diferent 1https://github.com/clarin-eric/parla-clarin 2The data will be published openly using the CC BY 4.0 license by the end of 2022. 3https://www.clarin.eu/resource-families/parliamentary-corpora 4http://data.camera.it datasets, such as [23, 24, 25].

In Finland, parliamentary materials have been digitized and utilized to some extent in DH and social science research. For example, [26] examines the diferences in political speech between parties throughout the parliamentary period 1907–2018. In [27], the content of the plenary speeches given in Parliament in 1999–2014 were studied by using topic modeling. Also, in [28] the debates were examined. However, data have so far been used only in a few studies that deploy methods from corpus linguistics, language technology, or computer science [ 2 ].

Previous search applications for the Finnish parliamentary speech data are based mostly on traditional text search. However, search applications have been developed for other digitized and enriched Cultural Heritage datasets [29, 30]. The data analysis tools to examine the results are few, such as the concordance analysis of the Language Bank of Finland5, where the words are visualized in their textual contexts and show some statistics of occurrences in the search results. The Language Bank’s tool has many corpora and one small corpus covering a small part of the entire time series of the Finnish parliamentary speeches.

3. Datasets and Knowledge Extraction

3.1. Core Datasets

The ParliamentSampo system includes data about the MPs, parliamentary speeches, and political organizations within the PoF. The data covers also the comments of the Speaker (President) of the PoF and all other small comments recorded in the minutes, e.g., in connection with voting proceedings. The ParliamentSampo data contains two major parts: 1. The Prosopographic Knowledge Graph The Prosopographic Knowledge Graph [ 8 ] covers all MPs of Finland since the year 1907. At its core lies a RDF conversion of data about MPs from the originally XML-formatted Open Data service6 of PoF. In addition to basic information, such as times and places of birth and death, the data includes detailed information about politicians’ life events, such as studies, working life, political career, and their written publications. In addition to people, the graph contains information about organizations, professions, and positions, as well as places. Organizations include, e.g., parties, ministries, parliamentary groups, committees, and constituencies, as well as schools, organizations, and companies outside the political community.

2. The Parliamentary Speeches Knowledge Graph The knowledge graph of parliamentary speeches contains speeches collected from all the minutes of the plenary sessions of the PoF since 1907 [ 7 ]. This knowledge graph was compiled from the documents available on the Open Data services7 and web sites8 of the PoF. Depending on the time period they covered, the documents were available in diferent formats: PDF, HTML, or XML. PDF documents were transformed into text with OCR.

In addition to the actual speeches, the speech graph contains all the relevant metadata attached to the minutes, such as interjections, information about the session where the speech 5https://www.kielipankki.fi/support/access/ 6https://avoindata.eduskunta.fi/#/fi/dbsearch 7https://avoindata.eduskunta.fi/#/fi/home 8https://www.eduskunta.fi/fi/Sivut/default.aspx was given (time, date, serial number, etc.), speaker information (name, role, party) and possible topic of discussion, and supporting documents (e.g. committee report). Based on the metadata, the speeches were linked to the MPs P-KG. For example, speakers and the parties they represent are resources with URI identifiers described in the P-KG.

3.2. Knowledge Extraction

In our work, the speech knowledge graph was enriched using various NLP methods. The toolset that was used in enriching the BiographySampo dataset [31, 32] was re-used together with new methods for NER, lemmatization, and automatic subject indexing. The parliamentary debates were enriched with named entity recognition (NER) and linking, subject indexing, and by creating a linguistic knowledge graph containing linguistic details for the speeches. Here, NLP methods were used on a subset of the speeches dataset, consisting of speeches from parliamentary session 2015 to the end of parliamentary session 2021, totaling in a little over 114 000 speeches, covering about 12% of the speeches dataset.

Lemmatization and Subject Indexing The Secompling9 was used for the tasks of lemmatization and subject indexing. It is an under-development library, which aims at integrating diferent Finnish NLP tools.

Lemmatization can be seen of as a kind of text normalization, especially for a language as morphologically rich as Finnish, which has 15 inflectional cases and a rich system for derivative words. Lemmatization enables exact term-based search instead of wildcard-based stemming. Lemmatization allows word count-based algorithms, such as TF-IDF, to work with more precision. Secompling employs the Turku Neural parser pipeline [33, 34] for lemmatization, and Voikko10 and uralicNLP [35] to check and possibly fix errors regarding these base forms. The Secompling lemmatization module has not been formally evaluated yet.

Subject indexing allows texts to be described succinctly by focusing on keywords that best characterize their contents. In our work, the subject indexing tool Annif [36], developed by the National Library of Finland, is used for this task. As Annif is capable of using machine-learningbased correlational backends such as Parabel, it may sometimes suggest NEs not mentioned in the texts. Since we focus on entities that are actually mentioned in the speeches, NEs from Annif were ignored. The other subject keywords are filtered out according to their weight. The keywords provided by Annif are entities from the General Finnish Ontology YSO11 – which is part on the national Finnish LOD infrastucture [37] – with ready-to-use URIs for data linking.

A total of 10467 keywords were identified for this dataset, with an average of 23.23 keywords per text, a maximum of 78, a minimum of 1 and a standard deviation of 8.46. The most common subjects were poliitikot ’politicians’ (around 49% of the texts), ministerit ’ministers’ (ca. 45%), kunnat ’municipalities’ (ca. 42%) and lainsäädäntö ’legislation’ (ca. 39%).

Named Entity Recognition and Linking NEL was performed on the speeches to improve data browsing and searching in the ParliamentSampo portal. Similarly to the analytics done for the textual biographies in the BiographySampo [32] system, NEL enables more detailed data analytics in the ParliamentSampo dataset, too. NEs were extracted using the Nelli [38, 39] 9https://version.aalto.fi/gitlab/seco/secompling 10https://voikko.puimula.org/ 11https://finto.fi/yso/fi/new?clang=en tool and its results linked using the ARPA. Unlike in the BiographySampo dataset, here Nelli was configured to use FinBERT’s combined NER model [ 18 ], Reksi [38], and the Turku Neural parser pipeline. FinBERT’s NER tool that is coupled with Reksi to pick up links to legislation, references to various dates, and identifiers (e.g., URLs). The Turku Neural parser was selected for morphological analysis based on its performance [33]. These tools extracted entities that were later linked using ARPA to the ParliamentSampo dataset and to other external datasets, such as the Kanto ontology of Finnish actors12, the Place Name Register PNR ontology of contemporary Finnish places13, and the YSO places14 ontology that contains also historical places mentioned in the speeches.

The tools used for NER managed to extract NEs from 89% of the speeches of which 30% contained people, 19% mentions of time, 12% organizations, and 7% of places. However, the linking of entities requires still some work. For example, the full name references to MPs were linked while the surname references were not. The place mentions were linked mostly correctly, however, the target ontologies lacked some mentioned place names like Wuhan.

Morpholinguistic Knowledge Graph Lastly, the speeches were transformed into a separate morpholinguistic knowledge graph (MLKG) containing detailed linguistic and morphological information about the speeches using a pipeline previously used for BiographySampo [40]. This graph can be used for linguistic analysis of the parliamentary speeches similarly to the work done in BiographySampo [32]. For example, in the BiographySampo dataset, it was noticed that biographies of women contained more family-related terminology while biographies about men used more words related to war and religion. In order to apply same methods to parliamentary speeches, a similar pipeline was used, updated to use the Turku Neural parser pipeline, and adjusted to handle larger datasets in smaller chunks of text. In this case, it was configured to process data by year. The results are also linked to the ParliamentSampo speeches dataset to enable analysis of speeches using the speech metadata.

4. Using the Enriched Data in ParliamentSampo

The enriched ParliamentSampo data is used in the development of the ParliamentSampo portal [ 6 ] which is based on the Sampo model [41] and the Sampo-UI framework [42]. The portal demonstrates how the data service can be used for developing applications for DH research. In this application the data can be browsed using ontology-based faceted search, and the results can then be analyzed with the integrated visualization and data analysis tools.

The enriched data is initially used to boost the browsing and searching capabilities of the portal. For instance, NEs and keywords can be used via facets to find speeches that mention a specific topic or a NE, such as a place or an organization. Coupled with the speeches, their metadata, and the prosopographical data, this enables studying, e.g., how MPs talk about matters related to their constituency. At the moment mentioned organizations and places have already been added into portal as facets to test the data.

Currently, the MLKG about the speeches is limited to a few years. It is not yet included in the 12https://finto.fi/finaf/fi/ 13https://www.ldf.fi/dataset/pnr/ 14https://finto.fi/yso-paikat/en/ ParliamentSampo portal, but we plan to add it and develop similar linguistic analysis views as in BiographySampo. This enables, e.g., to study the vocabulary used by the MPs and parties in their speeches. It is also possible to compare diferences in vocabulary of men and women.

In addition, the SPARQL endpoint underlying the ParliamentSampo portal can be used for querying, analyzing, and visualizing the enriched data. In Fig. 1, e.g., the speeches mentioning Finland’s neighbouring countries Norway, Sweden, Estonia, and Russia are counted on a yearly basis and plotted from 2015 to 2021. The plot shows that Sweden appears more frequently than the other neighbours. Russia is also mentioned increasingly in 2020 and 2021. Based on initial analysis of the speeches mentioning Russia and Sweden, the discussions and their frequencies are related to topics such as the annexing of Crimea, managing good relations, defence and security of Finland and its nearby areas, such as the Baltic sea. These mentions reflect the working order of the parliament, the domestic and world events described in the media at the time. It remains as future work to study the context of these mentions in more detail. Similarly, by linking to place ontologies it is possible to leverage the benefit of organized information to create visualizations that cluster all, e.g., Russia-related place names as mentions about Russia. It also enables the use of map-based visualizations.

5. Discussion

In this paper, we presented work for enriching the Finnish parliamentary debate corpus to support data browsing and using it for DH research. This is ongoing work that still requires adjustments and extensive evaluation, similarly, the ParliamentSampo portal is still under development. The tools used in the enrichment have been previously evaluated with diferent corpora, but not for the parliamentary data. The FinBERT NER tool has achieved an accuracy of 93.11% using the combined model in cross-corpus evaluation [ 18 ]. Similarly, the Turku Neural Parser pipeline is evaluated based on CoNLL 2018 UD Shared Task [43] with accuracy of LAS15 86.60%, UPOS16 96.66%, and XPOS17 97.63% [33]. Subject indexing is dificult to evaluate, however based on evaluation of the Annif tool, its accuracy is 30–50% depending on the test corpus [44]. These results have been produced on formal Finnish language texts similar to the Finnish parliamentary debates corpus.

15Labeled attachment score (LAS) is the proportion of words that have connected correctly the head word with the right dependency relation.

16Universal part- of-speech tagging 17Language-specific part-of-speech tagging

The data has been partially added to the ParliamentSampo knowledge graph and utilized already in the facets of the semantic portal. The enriched data enables DH research through topics and NEs. The enrichments help to find interesting phenomenon in the ParliamentSampo dataset. Similarly to the Finnish dataset, NEs have been added, e.g., into the Dutch and Latvian parliamentary debate corpora. The linked NEs and keywords enable data analytics and search optimization in the faceted search application. The MLKG contains millions of triples of morphological and linguistic information as linked data. Unlike, e.g., Slovenian debate corpora, the Finnish dataset can be queried directly using SPARQL to analyze speeches using also the metadata. However, due to size of dataset, there is still much work to be done to speed up the queries. It remains future work to create applications for the DH community to enable to study the debates in more detail.

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