Resolving Elliptical Compounds in G erman Medical Text (2023)

Abstract: Elliptical coordinated compound noun phrases (ECCNPs), a special kind of coordination ellipsis, are a common phenomenon in German medical texts. As their presence is known to affect the performance in downstream tasks such as entity extraction and disambiguation, their resolution can be a useful preprocessing step in information extraction pipelines. In this work, we present a new comprehensive dataset of more than 4,000 manually annotated ECCNPs in German medical text, along with the respective ground truth resolutions. Based on this data, we propose a generative encoder-decoder Transformer model, allowing for a simple end-to-end resolution of ECCNPs from raw input strings with very high accuracy (90.5% exact match score). We compare our approach to an elaborate rule-based baseline, which the generative model outperforms by a large margin. We further investigate different scenarios for prompting large language models (LLM) to resolve ECCNPs. In a zero-shot setting, performance is remarkably poor (21.6% exact matches), as the LLM tends to apply complex changes to the inputs unrelated to our specific task. We also find no improvement over the generative model when using the LLM for post-filtering of generated candidate resolutions.

## introduction coordination ellipses are a common phenomenon in many human languages, where certain words or phrases are omitted in a sentence due to their repetition from a previous or subsequent clause in a conjunction. usually, we assume that the reader can infer the omitted words from the context. in the biomedical domain, the presence * authors marked by * equally share first authorship. @xcite complex ellipsis suffix: "-positivität" prefix: "hpv-" figure 1 : examples for eccnps, their annotation and resolution in ggponc, a corpus of german oncology guidelines @xcite of coordination ellipses has been shown to negatively affect downstream tasks such as named entity recognition (ner) @xcite and entity linking @xcite . in this work, we consider the special case of elliptical coordinated compound noun phrases (eccnps) in the german language, where they are commonly encountered in biomedical and other scientific texts, due to the high density of contained information and their compact form of writing. for instance, consider doctor's letters and medical reports from kidney transplant patients or cancer patients. following the terminology introduced by @xcite , we distinguish forward, backward and complex ellipses, as shown in fig. 1 . the resolution of eccnps is particularly challenging in german due to its closed noun compounds, i.e., noun compounds without separating space. recently, an increasing number of german medical corpora with semantic annotations have been released (cf. zesch and bewersdorff 2022 for a recent survey). the annotation schemes of some of these corpora, including bronco @xcite as well as the german subset of the mantra gsc @xcite , support fragmented, i.e., non-contiguous, entity annotations. however, the annotations in these corpora do not reveal which of these are eccnps, as non-contiguous entity spans may also occur for various other reasons. at the same time, there may be further eccnps that are not among the annotated named entities. in the absence of a suitable dataset for training and evaluating eccnp resolvers for german medical language, we provide a new, comprehensive dataset based on a manual re-annotation of a subset of ggponc 2.0 @xcite . our annotated dataset, source code, and trained models are available online @xcite . key contributions of this work are: the remainder of this work is structured as follows: in section 2 we set our work in the context of related work. in section 3 we present our novel dataset and the employed methods. we evaluate the performance of different approaches for resolving eccnps in section 4. we discuss our findings in section 5 and conclude our work with an outlook in section 6. ## related work the identification and resolution of various kinds of coordination ellipses have been studied extensively. it is generally assumed that their presence leads to ambiguity, and some authors have even considered them as instances of ill-formedness @xcite . in the biomedical domain, their relevance was mostly discussed in the context of named entities and the resulting ambiguities in their detection and normalization. @xcite address the problem of resolving coordination ellipses inside named entities. their approach identifies potential candidate spans through an ner step, followed by the identification of conjuncts, and a rule-based heuristic to resolve the ellipses. for identifying conjuncts, the authors find that a conditional random field (crf) outperforms a rule-based baseline. @xcite evaluate different types of features for the conjunct identification step. similarly, @xcite use a crf with a variety of morphosyntactic and semantic features for recognizing coordination ellipses, followed by a rule-based approach for reassembling the segmented tokens. @xcite provide an extensive formal analysis of various types of omissions in coordinated noun phrases and propose a rule-based algorithm, also relying on the identification of named entities (here with a dictionary). @xcite consider the special case of eccnps and use a dependency parser for identifying candidate terms, and then generate potential resolutions using syntactical and semantic information. @xcite propose receem, an unsupervised, graph-based approach for reconstructing elliptical named entities in previously identified entity spans. importantly, they show the positive effect of resolving such ellipses for downstream entity linking performance. all the aforementioned approaches build on rather complex multi-stage pipelines with a range of assumptions. most of them consider ellipses inside named entities only and rely on ner to identify candidate spans. they also employ different heuristics, usually based on syntactic information provided through part-of-speech (pos) tagging and dependency parsing. furthermore, most of the existing approaches assume that conjuncts are given as individual tokens, with @xcite being a notable exception. this might be a sensible assumption for the english language, but it makes it difficult to apply these approaches to languages where compound nouns are usually not tokenized (such as german). to the best of our knowledge, @xcite have proposed the only algorithm that deals with eccnps for german text, albeit not in the biomedical domain. we use this work as a baseline to evaluate our approach and thus describe it in further detail in section 3.2. in contrast to prior work, our proposed generative approach treats the problem in an end-to-end fashion by directly resolving all eccnps in an input sentence. therefore, it does not rely on any particular tokenization or segmentation of the input. we note that @xcite have proposed a similar generative approach, although they consider different types of ellipses in task-oriented dialogue and rely on dialogue context. ## materials and methods in the following, we describe our contributed dataset, different methods for resolving eccnps, and metrics for the evaluation of such methods. ## results and evaluation ## discussion in the following, we further discuss our findings and potential alternative experimental designs. ## conclusion and future work in this work, we have presented a novel dataset and an end-to-end approach for the task of resolving eccnps in german medical text. a supervised encoder-decoder approach yields strong results on this task, substantially outperforming prior work, which was rule-based. while the general-purpose model mt5 worked well in our initial experiments, a systematic evaluation and hyperparameter optimization for other architectures and checkpoints from the increasingly large pool of text generation models may yield further improvements, e.g. the evaluation of larger versions of mt5 (large, xl, or xxl) with our pipeline. although the initial zero-shot results with gpt-3.5 were rather disappointing, they could potentially be improved using more capable llms -at the time of this writing, access to the most recent release by openai (gpt-4) was still in a restricted rollout phase with a waiting list. such models might also benefit from fine-tuning on labeled examples. further investigation of the root causes for low exact match scores might also help in designing more appropriate prompts and choosing examples for in-context learning. additionally, we plan to investigate different ways to choose from the top k generated outputs of our encoder-decoder model, e.g., by framing it as a supervised multiple-choice or natural language inference problem. in an earlier study, we have shown that a modern, bert-based ner model was remarkably robust in the presence of coordination ellipses @xcite . however, other prior work suggests that the resolution of eccnps would still benefit downstream tasks like entity linking. we plan to systematically assess the impact of resolving eccnps in german text for such tasks in future work to determine the broad applicability of our approach. considering the simplicity of the encoderdecoder model, it can be easily integrated into clinical nlp pipelines, which are increasingly becoming available for the german language and medical domains, in particular oncology, cardiology, and nephrology.