Leveraging a Morphological Lexicon for a Semi-Automatic Approach to Correcting Lemmas and Morphosyntactic Tags
Leveraging a Morphological Lexicon for a Semi-Automatic Approach to Correcting Lemmas and Morphosyntactic Tags

Summary/Abstract: In the paper, we present a new semi-automatic approach to correcting lemmas and morphosyntactic tags. Unlike previous manual annotation approaches for Slovene corpora, the new method contains an additional step in which tokens and their automatically assigned lemmas and morphosyntactic tags are cross-referenced with the set of forms included in the Sloleks Morphological Lexicon of Slovene. Based on the comparison, each token is classified into one of several annotation scenarios. The new approach has noticeably reduced the time and resources invested into annotation by eliminating a large number of redundant tasks. The advantages of this method include the possibility of dividing annotation tasks into groups consisting of similar annotation problems (e.g. disambiguation of grammatical homographs). With adequate data preparation, it also drastically reduces the necessity for annotators to be familiar with the extensive Multext-East morphosyntactic tag set for Slovene, a restriction that created a bottleneck in the annotation process in similar annotation campaigns. The method was tested during the annotation process for the ROG Training Corpus of Spoken Slovene. In addition, we also test the scenario classification algorithm on the SUK Training Corpus of Written Slovene, which was annotated using the traditional sentence-by-sentence, token-by-token approach. We present the results and argue that the method should be used in future annotation campaigns to save resources and improve overall annotation consistency, while also discussing some of the caveats and disadvantages of the proposed approach.

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