# Breaking Down Finnish Compounds: Creating a Dataset for NLP

Melany Macias

Metropolia University of Applied Sciences

## Introduction

Finnish, like other Uralic languages, features complex morphology, especially in its use of compound words. Compounding allows speakers to create unique terms by joining existing words, but this flexibility poses challenges for natural language processing (NLP) due to the frequent absence of compounds in standard dictionaries. This project addresses these challenges by building a dataset that decomposes Finnish compound words into their component parts, creating a resource to support computational models and a range of NLP applications.

## Methodology

<figure><img src="image_1.png" /></figure>
The project begins with collecting diverse Finnish text
from sources such as news articles, books, and blog
posts. After extracting and tokenizing the text, we
remove non-alphabetic tokens to focus on relevant word
forms. To identify potential compound words, we filter
for words with more than ten characters, cross-
referencing each against a Finnish dictionary of known
words. Words absent from the dictionary are marked as
likely compounds, narrowing down the set for
decomposition.

For decomposition, we apply a dictionary-based lookup
method to split these compounds into meaningful
components. For each compound candidate, we attempt
all possible splits, checking if each part is a valid Finnish
word. For example, a word like "kaupunginsuunnittelu"
(city planning) decomposes into "kaupunki" (city) and
"suunnittelu" (planning). If no valid split is found, we
add an "s" as a linking vowel (a common feature in
Finnish compounds) or remove common suffixes like "-t"
or "-ssa" to improve matches. This process provides
accurate decompositions for even complex words,
resulting in a structured dataset of Finnish compounds
and their components.

This dataset has numerous NLP applications. Machine
translation systems can use it to improve the accuracy of
Finnish-to-English translations by preserving the
meaning of complex words, while search engines can
apply it to decompose queries and retrieve more
relevant results. For Finnish language learners, the
dataset serves as an educational tool, making it easier to
understand compound meanings and structure. It also
offers linguists insights into compound formation
patterns, supporting research in Uralic morphology and
syntax.
## Conclusions

A few studies have addressed Finnish compound decomposition directly, although compound word analysis has been explored in other morphologically rich languages, such as German and Turkish. For Finnish, prior work largely centers on lexical databases, like the Finnish Wordnet (Lindén & Carlson, 2010) and Omorfi (Open Morphology for Finnish) (Pirinen, 2015), which provide general morphological analyses but do not specifically decompose compounds into component parts. Most existing resources for Finnish compounds, including Omorfi, offer rules for generating compounds but are limited when it comes to decomposing or handling newly formed compounds, especially in diverse text sources.

To sum up, this project offers a foundational resource for computational work in Finnish. It enables better NLP applications, supports language learning, and improves understanding of Finnish compound morphology, making it a meaningful contribution to both linguistic research and practical applications.

## References

* Lindén, K., & Carlson, L. (2010). Finnwordnet–finnish wordnet by translation. LexicoNordica–Nordic Journal of Lexicography, 17, 119-140.

* Pirinen, T. A. (2015). Omorfi–free and open source morphological lexical database for Finnish. In Proceedings of the 20th Nordic conference of computational linguistics (NODALIDA 2015) (pp. 313-315).