Relevance Feedback Search Based on Automatic Annotation and Classification of Texts

Rafael Leal Tomas, Joonas Kesäniemi, Mikko Koho, Eero Hyvönen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)
82 Downloads (Pure)


The idea behind Relevance Feedback Search (RFBS) is to build search queries as an iterative and interactive process in which they are gradually refined based on the results of the previous search round. This can be helpful in situations where the end user cannot easily formulate their information needs at the outset as a well-focused query, or more generally as a way to filter and focus search results. This paper concerns (1) a framework that integrates keyword extraction and unsupervised classification into the RFBS paradigm and (2) the application of this framework to the legal domain as a use case. We focus on the Natural Language Processing (NLP) methods underlying the framework and application, where an automatic annotation tool is used for extracting document keywords as ontology concepts, which are then transformed into word embeddings to form vectorial representations of the texts. An unsupervised classification system that employs similar techniques is also used in order to classify the documents into broad thematic classes. This classification functionality is evaluated using two different datasets. As the use case, we describe an application perspective in the semantic portal LawSampo - Finnish Legislation and Case Law on the Semantic Web. This online demonstrator uses a dataset of 82145 sections in 3725 statutes of Finnish legislation and another dataset that comprises 13470 court decisions.
Original languageEnglish
Title of host publication3rd Conference on Language, Data and Knowledge (LDK 2021)
EditorsDagmar Gromann, Gilles Serasset, Thierry Declerck, John P. McCrae, Jorge Gracia, Julia Bosque-Gil, Fernando Bobillo, Barbara Heinisch
PublisherSchloss Dagstuhl - Leibniz-Zentrum für Informatik
Number of pages15
ISBN (Electronic)978-3-95977-199-3
Publication statusPublished - 2021
MoE publication typeA4 Conference publication
EventInternational Conference on Language, Data, and Knowledge - Virtual, Online, Zaragoza, Spain
Duration: 1 Sept 20213 Sept 2021
Conference number: 3

Publication series

NameOpen Access Series in Informatics (OASIcs)
PublisherSchloss Dagstuhl -- Leibniz-Zentrum für Informatik
ISSN (Electronic)2190-6807


ConferenceInternational Conference on Language, Data, and Knowledge
Abbreviated titleLDK


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