Compound or Term Features? Analyzing Salience in Predicting the Difficulty of G erman Noun Compounds across Domains (2021)

Abstract: Predicting the difficulty of domain-specific vocabulary is an important task towards a better understanding of a domain, and to enhance the communication between lay people and experts. We investigate German closed noun compounds and focus on the interaction of compound-based lexical features (such as frequency and productivity) and terminology-based features (contrasting domain-specific and general language) across word representations and classifiers. Our prediction experiments complement insights from classification using (a) manually designed features to characterise termhood and compound formation and (b) compound and constituent word embeddings. We find that for a broad binary distinction into ‘easy’ vs. ‘difficult’ general-language compound frequency is sufficient, but for a more fine-grained four-class distinction it is crucial to include contrastive termhood features and compound and constituent features.

## introduction in times of a constant growth of domain-specific data, it is more important than ever to analyse characteristics of domain-specific vocabulary. domains are topically restricted subject fields containing domain-specific vocabulary that encode domain knowledge. the more technical the terminology in the domain vocabulary, the more difficult it is perceived by lay people unfamiliar with the domain. predicting the difficulty of domain-specific vocabulary is therefore an important task for enhancing the communication between lays and experts. a prominent example in this respect is the medical domain, where the prediction of difficulty of medical terms can enhance the communication between doctors and patients, e.g. by simplifying medical texts @xcite @xcite . while the medical domain represents a well-researched focus, the problem of miscommunication appears across domains. previous research on automatic term difficulty prediction already explored a large number of parameters, but as to our knowledge there is yet no study that investigated how difficulty can be attributed to complex phrase formation processes (a language phenomenon) in interaction with domain specialization (a domain phenomenon). the current study investigates these aspects, goes beyond domain peculiarities (such as latin words in the medical domain), and performs analyses across three rather different domains: cooking, diy ('doit-yourself') and automotive. while we choose a diverse set of domains, we otherwise focus on a special phenomenon within domain-specific vocabulary: german closed compounds. closed compounds are complex expressions that consist of several lexemes and are written in a single string of characters. an example is bremsflüssigkeit 'brake fluid', which is composed of the two simple words bremse 'brake' and flüssigkeit 'fluid'. by focusing on closed compounds, the boundaries of the phrases to pre-extract in text are unambiguous, and feature analysis will not be biased by how the extraction method is designed. furthermore, closed compounds are a frequent phenomenon in german: @xcite found that 47% of the word types in a generallanguage corpus in german are compounds, and according to clouet and daille (2014) compounding is even more productive in specialized domains. the interaction of domain features and lexical features can be easily demonstrated at the examples of closed compounds: for example, the compound hydraulikleitung 'hydraulic line' is considered difficult because it contains the rather technical constituent 'hydraulic'. in contrast, the compound blaukochen (lit: 'blue boiling', a special kind of boiling fish by adding acid) only contains con-stituents that are well-known to lay people but is nevertheless difficult for them because the compound is not semantically transparent regarding its constituent 'blue', i.e. it is not obvious what the constituent contributes to the meaning of the compound. in sum, the difficulty of a compound cannot be derived from only compound attributes; in addition, it is influenced by the role and properties of the constituents. in this study, we want to empirically investigate how phrase formation and domain-specific termhood 1 attributes interact in the automatic prediction of compound difficulty. in order to train predictive models, we use a german compound dataset with a total of 1,030 compounds across the above-mentioned three domains. based on two settings of the gold standard dataset (a four-class and a binary version) we apply a decision tree classifier using manually designed features to characterize termhood and compound formation, and neural classifiers using word embeddings. ## related work term difficulty prediction (also referred to as term familiarity or term technicality prediction) can be seen as a subtask of automatic term extraction. for automatic term extraction, a major strand of methodologies are contrastive techniques, where a term candidate's distribution in a domain-specific text corpus is compared to the distribution in a reference corpus, for example a general-language corpus @xcite @xcite @xcite @xcite . many term difficulty prediction studies rely on some variant of contrastive approaches, mostly frequency-based; notable exceptions are @xcite , who apply a contextual network, and @xcite , who use a likelihood ratio test based on two language models. most studies fall into the medical, biomedical or health domain. they rely on classical readability features such as frequency, term length, syllable count, the dale-chall readability formula or affixes @xcite @xcite . some features are tailored to the medical domain, for example relying on neo-classical word 1 termhood refers to the degree to which a lexical unit can be considered a domain-specific concept @xcite . as to our knowledge, there is no previous work that investigated term difficulty prediction for complex phrases. regarding the more general task of automatic term extraction, a few studies included complex phrases and their constituents. for example, the c-value @xcite combines linguistic and statistical information and takes nested terms into account for evaluating termhood. the fgm score @xcite relies on the geometric mean of the number of distinct left and right neighboring words for each constituent in a complex term. contrastive selection via heads (csvh) @xcite ) is a corporacomparing measure that computes termhood for a complex term by biasing the termhood score with the general-language frequency of the head. @xcite combine termhood measures within a random forest classifier to extract single and multiword terms and apply the measures recursively to the components. hätty and schulte im walde (2018) demonstrate that propagating constituent information through neural networks improves the prediction of compound termhood. ## experiments on predicting difficulty our prediction experiments investigate and complement insights from decision tree classification using manually designed features to characterise termhood and compound formation (section 4.1), and logistic regression (lr) and multilayer perceptron (mlp) classification using compound and constituent word embeddings (section 4.2). for evaluation, we use 5-fold cross-validation and micro-and macro-f1 score. as a comparison to the model results, we apply a majority-class baseline. when testing for significance, we use the mcnemar's significance test @xcite , a paired non-parametric statistical hypothesis test. ## conclusion this study investigated the automatic prediction of difficulty for domain-specific german compounds across three domains. we asked to what extent compound formation attributes and domain-specific termhood attributes influence and interact in the prediction. we found that plain general-language compound frequency is a reliable indicator for difficulty in our dataset, which shows that effects of domain-specialization and compound formation are reflected to a large extent by general corpus frequency. however, for a more fine-grained fourclass distinction of difficulty going beyond a broad binary distinction into 'easy' and 'difficult', contrastive termhood features and compound and constituent information are crucial.