T ermo UD - a language-independent terminology extraction tool (2023)

Abstract: The paper addresses TermoUD — a language-independent terminology extraction tool. Itsprevious version, i.e. TermoPL (Marciniak et al., 2016; Rychlik et al., 2022), uses languagedependent shallow grammar which selects candidate terms. The goal behind the development of TermoUD is to make the procedure as universal as possible, while taking care of the linguistic correctness of selected phrases. The tool is suitable for languages for which the Universal Dependencies (UD) parser exists. We describe a method of candidate term extraction based on UD POS tags and UD relations. The candidate ranking is performed by the C-value metric (contexts counting is adapted to the UD formalism), which doesn’t need any additional language resources. The performance of the tool has been tested on texts in English, French, Dutch, and Slovenian. The results are evaluated on the manually annotated datasets: ACTER, RD-TEC 2.0, GENIA and RSDO5, and compared to those obtained by other tools.

## introduction the purpose of automatic term extraction (ate) is to identify recurring phrases that are relevant to the domain of a given text. such phrases can then be interpreted as candidates for key phrases, index terms or potential domain lexicon entries. the first among many approaches to this problem is selecting term candidates based on one of the following methods: n-grams @xcite ; a set of patterns defining sequences of part-of-speech (pos) tags allowed within phrases @xcite ; phrases identified by a syntactical parser or an npchunker @xcite . all the generated candidate terms are then ranked with an ordering procedure based, among other things, on tf/idf @xcite , the c-value @xcite , or the mutual information value. the top elements of the obtained list are treated as domain terminology. methods based solely on n-grams are language in-dependent but achieve worse results (especially for inflectional languages) than those based on shallowparsers which are language dependent. one of the important objectives of developers of ate tools is to make them language independent. the jate system @xcite selects candidate terms on the basis of syntactic analysis. however, it is designed to make it easy to adapt to different domains and/or languages -a flexible mechanism to determine how candidate phrases are constructed has been defined. the second approach to ate involves combining solely statistical features extracted from the processed text used in heuristics selecting terminology. an example of this approach is yake! @xcite , which supports 9 languages. the score assignment in yake! combines features such as letter case, a position within the text, word frequency and the number of different sentences in which a given term appears and, finally, the number of different contexts in which a term appears. scores for 1-grams are combined to give the ranking of n-grams. the method gives good results for keyphrase extraction from short texts. the method doesn't work so well for inflection-rich languages, as the statistics are counted on forms, moreover; the processing of texts longer than a few pages is time-consuming. the newest approach solves the problem of terminology extraction as a sequential tagging task and applies machine learning methods similar to the named entities recognition. this approach was first used in the termeval 2020 shared task @xcite , whose organisers published english, french and dutch data collected in the annotated corpora for term extraction research (acter) @xcite . all of the above approaches have various limitations. although the latter approach has proven quite effective on the acter corpus, its use is limited to cases where we have annotated data, which are quite rare. using only statistical heuristics to identify phrases has proven ineffective, especially for highly inflected languages, and defining pos patterns or grammatical rules requires knowledge of the language in question. here we propose a method that, while not completely universal, can be used without additional modifications or resources for a great many languages. our tool for terminology extraction performs selection of candidate terms by using dependency parsing. the presented method is language independent and time-efficient. nowadays, dependency parsers are very popular and are available for many languages (e.g. spacy works for 20, and stanza for 70 languages) and are robust enough to be used in nlp applications. they are quite naturally used a lot in relation extraction, e.g. @xcite or @xcite , but there is still little interest in using dependency parsing in terminology extraction. the only two known approaches are @xcite and @xcite . gamallo used dependency parsers for bilingual term alignment. in the second paper, the authors used dependency parses for candidate selection for chinese and achieved better results (both in terms of recall and precision) than using only pos based rules. in @xcite , the authors proposed the post-processing of selected phrase-candidates by checking the consistency of dependency parses of already selected phrases. ## extraction process to make our program as universal as possible, we had to define a set of rules to identify, without any changes, noun phrases in dependency trees constructed by parsers processing sentences in different languages. ## evaluation to compare the results with other approaches, we evaluate our tool on the following corpora annotated with terminology: acter, genia, acl rd-tec, and rdso5. so, we tested the method on four languages: english, french, dutch and slovenian. for comparison, the d-terminer @xcite and sketch engine @xcite were also used to process the same datasets. in the case of termoud, we tested the plain tool without additional existing filters developed for termopl, e.g., removing stopwords from candidate terms. the result of termoud is a sorted list of all detected phrases, with no indication of where a split between terms and non-terms is suggested. since the ranking method used in the tool assigns the same values to many terms, the evaluation cannot be carried out at any point in the ranking list, but only in those places where the value changes. therefore, it is not possible to compare our method with others for the lists of terms of the same length. ## conclusion and future work termoud's method of selecting traditional candidate terms restricted to nominal phrases allows multiple languages to be processed with the same tool. as linguistic knowledge is already contained in the ud parsers, no language adjustments are needed. for example, it is irrelevant whether adjectives can come before or after a noun in a given language. an additional, unique feature of ud-based candidate term selection is its ability to extract discontinuous phrases, see figure 1 . the best current methods of terminology extraction use machine learning and sequential tagging. the results obtained by these methods are much better than termoud's, especially measured by precision. these methods also facilitate the expansion of term types, e.g. to include those which are adjectives and verbs. however, the methods require the preparation of training data, that exists for only a few languages, text types and domains. the quality of the results obtained by termoud depends on the quality of the parser for the language in question, especially how good the lemmatisation is. this feature is particularly important for languages with rich morphology, as we need to recognise and join various inflected forms of candidate terms. the important feature that differentiates the two approaches is the list of results. for the classification-based methods, we get a list of terms, whereas termoud generates a sorted list of all term candidates. the disadvantage of the termoud tool is the need to establish where the list is divided into terms and non-terms, but the advantage is we can choose how many of the candidates we would like to choose. machine learning methods only provide a list of accepted terms, which is fragmentary knowledge as we do not know the phrases that were rejected and should have been classified as terms. as the ranking coefficient used in ter-moud is highly dependent on term frequencies, our method gives much better results for larger data. terms used in text only once are always located very low on the final list. in the near future, we plan to deal with the analysis of coordinated phrases, which are quite a challenge for all terminology extraction tools and the ud mechanism seems to enable their correct handling. moreover we should test the tool on languages from other families, and improve the term ordering method. as our lists contain on average about 80% of terms, changes in ordering method may significantly improve the results. termoud foot_0 is available from http://zil. ipipan.waw.pl/termopl , the same page as ter-mopl, the previous version of the tool described in the paper. termopl is also a part of korpusomat @xcite , a simple tool for creating linguistic corpora in polish https: //korpusomat.pl/ . termoud will be available from the multilingual version of the korpusomat tool currently under development.