BioWiC construction

BioWiC³⁵ instances follow a similar format to WiC, where each instance involves a pair of biomedical terms (w₁ and w₂) and their corresponding sentences (s₁ and s₂). The task is to classify each instance as True if the target terms carry the same meaning across both sentences or False if they do not. We represent each instance as a tuple pair t = [(s₁,w₁),(s₂,w₂)]: y where w₁ and w₂ are the target terms, s₁ and s₂ are the corresponding sentences, and y is the associated binary label. Table 2 presents some examples of BioWiC instances. In contrast to WiC, where both target terms of each instance always share the same lemma, BioWiC allows for variations such as abbreviations, synonyms, identical terms, and terms with similar surface forms.

BioWiC³⁵ instances, drawn from the test split.

The target terms of each instance are in bold.

To evaluate challenging scenarios for semantic representation, such as synonymy, polysemy, and abbreviations, BioWiC³⁵ is divided into four main groups of instances. Group A (term identity) contains instances where the target terms w₁ and w₂ are identical. In group B (abbreviations), either w₁ or w₂ could represent the abbreviation of the other one. Group C (synonyms), consists of instances where w₁ and w₂ could be synonyms (according to UMLS). Lastly, group D (label similarity) includes instances where w₁ and w₂ share similar surface forms. We employed the following five steps to generate the BioWiC instances:

For clarity, in Fig. 2 we provide an example of building BioWiC³⁵ instances for the target term “delivery”. Initially, we preprocess the resource data and extract all sentences in which “delivery” is linked to UMLS. We transform each sentence to the sentence-term tuple (s_i,w) format where s_i represents a sentence containing the term w = “delivery”. Subsequently, we permute all possible combinations of tuples (s_i,w) identified in the preceding step to generate BioWiC instances t = [(s_i,w),(s_j,w)], where “delivery” serves as the target term in both sentences. Finally, we classify each instance as True when “delivery” is mapped to the same CUI code in both sentences and as False when it is not.

The overall pipeline of the BioWiC³⁵ construction process. Step 1: Pre-process the source documents to a consistent format. Step 2: Identify and retrieve sentences including the term “delivery” linked to UMLS. Step 3: Pair the retrieved sentences to generate BioWiC instances. In Step 3, the green box shows an example of a BioWiC instance with the same target concept, while the red boxes show examples of different target concepts.