An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text

Abstract

In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus.

MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain

“…Sarrouti et al (2022) compare various pre-trained transformer models. Semantic parsing of frame structures (Fillmore and Baker, 2001) has been addressed using graph-convolutional networks (Marcheggiani and Titov, 2020), BiLSTMs (He et al, 2018), and recently by generating structured output using encoder-decoder models (Hsu et al, 2022;Lu et al, 2021). Tackling semantic dependency parsing with a biaffine classifier architecture was first proposed by Dozat and Manning (2018).…”

Summarizing Procedural Text: Data and Approach

“…Procedural texts, e.g., scientific articles, instruction books, or recipes, are widely spread and useful in many real-world applications (Tang et al, 2020;Gupta and Durrett, 2019a;Du et al, 2019b). In procedural text modeling field, many research works focus on entity state tracking (Gupta and Durrett, 2019a,b;Swarup et al, 2020) and reasoning , and how to summarize the procedural text has not been fully explored. Since the procedural text contains many steps and the procedure is usually long, summarizing the procedural text can save time for readers when they want to quickly locate the useful step or take an overview of the procedural text.…”