Dealing with Abbreviations in the S lovenian Biographical Lexicon (2022)

Abstract: Abbreviations present a significant challenge for NLP systems because they cause tokenization and out-of-vocabulary errors. They can also make the text less readable, especially in reference printed books, where they are extensively used. Abbreviations are especially problematic in low-resource settings, where systems are less robust to begin with. In this paper, we propose a new method for addressing the problems caused by a high density of domain-specific abbreviations in a text. We apply this method to the case of a Slovenian biographical lexicon and evaluate it on a newly developed gold-standard dataset of 51 Slovenian biographies. Our abbreviation identification method performs significantly better than commonly used ad-hoc solutions, especially at identifying unseen abbreviations. We also propose and present the results of a method for expanding the identified abbreviations in context.

## introduction abbreviations such as "b." for "born", or "gr." for "graduated" are a common issue when dealing with digitized texts which use a large number of them for space-saving reasons. they are also a known problem when processing technical documents @xcite and biomedical texts @xcite . in this paper, we examine the case of biographical dictionaries, i.e. collections of biographies that have been digitized, and, in particular, the slovenian biographical lexicon. to automatically extract facts from biographical texts, digital humanities researchers normally rely on out-of-the-box nlp tools such as stanza @xcite or spacy. 1 these tools are often adequate for identifying sentences which are then used as input for higher-level downstream tasks, for manual inspection, and for visualization purposes. however, out-of-the-box tools are designed to work 1 https://spacy.io/ for the broadest possible text domains and cover the most common cases. this impacts performance significantly when dealing with domain-specific data, such as entries of biographical dictionaries, and more so when they contain a lot of abbreviations. the problem is even more pronounced when dealing with a relatively lower resource language, such as slovenian. the performance bottleneck occurs already at the first step, i.e. tokenization: in order to perform good tokenization in domain-specific texts, we need to have a reliable method for identifying abbreviations such that the tokenizer does not split them wrongly, generating faulty tokens and incomplete sentences. in this paper we: another common way to address the difficulty of domain-specific texts is text normalization, which is the task of translating a domain-specific text into more standard form (this can be at different levels such as lexical or morphological) that is easier to process by general purpose nlp tools. this approach is common when dealing with user generated text and social media @xcite @xcite , and also historical texts @xcite @xcite . one drawback of text normalization is that it is frequently implemented using an encoder-decoder approach @xcite , which requires a big-enough parallel corpus to be trained and obtain good-quality results. another drawback is the fact that it generates a new standard sentence, which is not a desired side effect if we want to preserve word by word the original biographical text. in contrast to the normalization task, we are interested in preserving the original text and only identifying (and perhaps expanding) the abbreviations that are problematic for the nlp tools. ## dataset the slovenian biographical lexicon (sbl) was published in 15 volumes and contains 5,047 biographies @xcite . for the experiments described in this paper, we have created the dataset sbl-51abbr @xcite , which consists of 51 randomly selected entries from sbl 3 . the text of each entry is manually tokenized and sentence segmented, marked with named entities, and lemmatized words. it has also been automatically annotated with universal dependencies pos tags, morphological features and dependency parses using classla @xcite ), foot_2 a fork of the stanford stanza pipeline @xcite ), foot_3 which is the state-of-theart tool for annotating slovenian. crucially for the envisaged use of the corpus, the abbreviations in the corpus have been manually expanded so that the expansions are in the correct inflected form. the curated dataset consists of 655 sentences (see we use this dataset as a gold standard to test the performance of our proposed methods. we randomly split the available data into three portions: 70% for training, 10% for development and 20% for testing. ## impact of abbreviations we first quantify the impact that the high number of abbreviations in the sbl-51abbr corpus has when processing the raw texts with classla @xcite and performing ner. we compare the performance on the original texts (with all abbreviations) with a second scenario where abbreviations were substituted with their gold expansions. corpus-based: we tokenize the training corpus using classla and compute the frequency for all token unigrams t 1 and bigrams (t 1 , t 2 ) in the corpus. if a bigram contains a full stop as a second component or if a unigram has a full stop as its last character, foot_6 then we increase the count of t 1 in a otherwise we increase the count of t 1 in b. we take all t 1 s that appear in both lists and calculate their probability to be an abbreviation as: if the probability p (t 1 = abbr) ≥ 0.8 then t 1 is considered to be an abbreviation, otherwise we skip it. this method will, of course, carry over some of the tokenization mistakes. the reasoning behind this baseline is to capture the number of times a given token appears before a full stop compared to the total times it appears in the corpus. if it is the case that most of the occurrences of such a token are immediately followed by a full stop, then it is most likely an (unrecognized) abbreviation. ## conclusions in this paper we focused on the task of named entity recognition to quantify the impact of abbreviations in a text by comparing the performance of the classla ner tagger on the same sentences with and without abbreviations. we presented a gold-standard dataset consisting of 51 biographies in slovenian (a limited-resource language) in a specialized text domain. we also presented a method for automatically identifying abbreviations and expanding them without the need for a tokenizer. the biggest advantage of our method is that it can be applied out-of-the-box for any language which has a large language model available and does not need ad-hoc training data or large fixed dictionaries. our abbreviation identification classifier obtains better precision and better recall when compared to other straightforward approaches to identify abbreviations. finally, we presented a method that uses a pretrained language model to predict plausible expansions for the identified abbreviations. we notice that our method is still simple but already achieves better results than directly processing the original sentences with abbreviations. in future work we aim to explore more sophisticated methods for abbreviation expansion that allow us to further improve the readability of texts. we find our results encouraging for researchers working with limited domains who may find a similar approach helpful for improving performance in other tasks.