The Icelandic Gigaword Corpus was first published in 2018. Since then new versions have been published annually, containing new texts from additional sources as well as from previous sources. This paper describes the evolution of the corpus in its first four years. All versions are made available under permissive licenses and with each new version the texts are annotated with the latest and most accurate tools. We show how the corpus has grown almost 50% in size from the first version to the fourth and how it was restructured in order to better accommodate different meta-data for different subcorpora. Furthermore, other services have been set up to facilitate usage of the corpus for different use cases. These include a keyword-in-context concordance tool, an n-gram viewer, a word frequency database and pre-trained word embeddings.

We describe the acquisition, annotation and encoding of the corpus of the Althingi parliamentary proceedings. The first version of the corpus includes speeches from 1911-2019. It comprises 406 thousand speeches and over 219 million words. The corpus has been automatically part-of-speech tagged and lemmatised. It is annotated with extensive metadata about the speeches, speakers and political parties, including speech topic, whether the speaker is in the government coalition or opposition, age and gender of speaker at the time of delivery, references to sound and video recordings and more. The corpus is encoded in accordance with the Text Encoding Initiative (TEI) Guidelines and conforms to the Parla-CLARIN schema. We plan to update the corpus annually and its major versions will be archived in the CLARIN.IS repository. It is available for download and search using the KORP concordance tool. Furthermore, information on word frequency are accessible in a custom made web application and an n-gram viewer.

We introduce an array of open and accessible tools to facilitate the use of the Icelandic Gigaword Corpus, in the field of Natural Language Processing as well as for students, linguists, sociologists and others benefitting from using large corpora. A KWIC engine, powered by the Swedish Korp tool is adapted to the specifics of the corpus. An n-gram viewer, highly customizable to suit different needs, allows users to study word usage throughout the period of our text collection. A frequency dictionary provides much sought after information about word frequency statistics, computed for each subcorpus as well as aggregate, disambiguating homographs based on their respective lemmas and morphosyntactic tags. Furthermore, we provide n-grams based on the corpus, and a variety of pre-trained word embeddings models, based on word2vec, GloVe, fastText and ELMo. For three of the model types, multiple word embedding models are available trained with different algorithms and using either lemmatised or unlemmatised texts.

We present ParIce, a new English-Icelandic parallel corpus. This is the first parallel corpus built for the purposes of language technology development and research for Icelandic, although some Icelandic texts can be found in various other multilingual parallel corpora. We map out which Icelandic texts are available for these purposes, collect aligned data and align other bilingual texts we acquired. We describe the alignment process and how we filter the data to weed out noise and bad alignments. In total we collected 43 million Icelandic words in 4.3 million aligned segment pairs, but after filtering, our corpus includes 38.8 million Icelandic words in 3.5 million segment pairs. We estimate that approximately 5% of the corpus data is noise or faulty alignments while more than 50% of the segments we deleted were faulty. We estimate that our filtering process reduced the number of faulty segments in the corpus by more than 60% while only reducing the number of good alignments by approximately 8%.