This paper describes our submission for the ScienceIE shared task (SemEval-2017 Task 10) on entity and relation extraction from scientific papers. Our model is based on the end-to-end relation extraction model of Miwa and Bansal (2016) with several enhancements such as semi-supervised learning via neural language models, character-level encoding, gazetteers extracted from existing knowledge bases, and model ensembles. Our official submission ranked first in end-to-end entity and relation extraction (scenario 1), and second in the relation-only extraction (scenario 3). The ScienceIE shared task (Augenstein et al., 2017) focuses on information extraction from scientific papers. In the end-to-end evaluation scenario, participants were provided with a paragraph and asked to extract typed entities (Task, Material or Process) and relations (Hyponym-of or Synonym-of) in different scientific domains. Running example. Consider the following input sentence: "Here, we consider a radical pair in which the first electron spin is devoid of hyperfine interactions, while the second electron spin interacts isotropically with one spin-1 nucleus, e.g. nitrogen." The provided human labeled entity annotations for this sentence include: "electron spin" of type Process, "spin-1 nucleus" of type Material, and "nitrogen" of type Material. The only positive relation annotation labeled for this sentence is: Hyponym-of("nitrogen", "spin-1 nucleus"). We will use this example throughout the paper to illustrate various parts of our system (see Fig. 1 ). In this section, we describe the components in our system. Text preprocessing. We process the input text using spaCy 1 which provides sentence segmentation, tokenization, part-of-speech (POS) tagging and labeled dependency parsing. Label encoding. We use the BILOU tagging scheme to encode the annotations for each of the three entity types. For a given entity type, each token in a sentence is labeled with: O if it does not belong to any entity, U if it belongs to a singletoken entity, B if it is the beginning of a multi-word entity, L if it is the end of a multi-word entity, and I if it is inside a multi-word entity. In order to allow the same token to participate in multiple entities of different types, each token is assigned three labels (one for each entity type). 2 The labeled data specify two kinds of relations: a directional relation (Hyponym-of) and an undirectional relation (Synonym-of). In our system, we automatically convert Hyponym-of(e2,e1) into a new label Hypernym-of(e1,e2) when e2 follows e1 in the sentence. We generate the label No-relation(e1,e2) as needed in order to have a label for each pair (e1, e2) where e1 precedes e2 in a sentence. This encoding allows us to only consider entity pairs in the order in which they appear in the sentence. In other 1 https://spacy.io/ 2 In a small number of cases, the human labels specified at the character level did not coincide with our tokenization. In these cases we used the human annotations to override the tokenization. When two entity annotations of the same entity type overlap, we only keep the longer one.
The second best system was a neural end-to-end model by REF .
The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction
SemEval@ACL
Computer Science