Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.
: has the purpose of breaking the text into tokens to be processed individually or as a sequence. These tokens are usually words but can also be phrases, numbers and other types of elements. The most straightforward form of tokenization is breaking the input text by the whitespaces or punctuation. However, with scientific biomedical literature, that is usually descriptive and formal, we have to account for complex entities like human phenotype terms (composed of multiple words), genes (represented by symbols), and other types of structured entities. These entities tend to be morphological complex and need specialized tokenization pipelines. Some researchers use a compression algorithm @cite_46 , byte pair encoding (BPE), to account for biomedical vocabulary variability. BPE represents open vocabularies through a fixed-size vocabulary of variable-length character sequences, making it suitable for neural networks models.
@cite_46
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Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem. Previous work addresses the translation of out-of-vocabulary words by backing off to a dictionary. In this paper, we introduce a simpler and more effective approach, making the NMT model capable of open-vocabulary translation by encoding rare and unknown words as sequences of subword units. This is based on the intuition that various word classes are translatable via smaller units than words, for instance names (via character copying or transliteration), compounds (via compositional translation), and cognates and loanwords (via phonological and morphological transformations). We discuss the suitability of different word segmentation techniques, including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm, and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU, respectively.