Scientific action graphs extraction from materials synthesis procedures is important for reproducible research, machine automation, and material prediction. But the lack of annotated data has hindered progress in this field. We demonstrate an effort to annotate Polycrystalline Materials Synthesis Procedures PcMSP from 305 open access scientific articles for the construction of synthesis action graphs. This is a new dataset for material science information extraction that simultaneously contains the synthesis sentences extracted from the experimental paragraphs, as well as the entity mentions and intra-sentence relations. A two-step human annotation and inter-annotator agreement study guarantee the high quality of the PcMSP corpus. We introduce four natural language processing tasks: sentence classification, named entity recognition, relation classification, and joint extraction of entities and relations. Comprehensive experiments validate the effectiveness of several state-of-the-art models for these challenges while leaving large space for improvement. We also perform the error analysis and point out some unique challenges that require further investigation. We will release our annotation scheme, the corpus, and codes to the research community to alleviate the scarcity of labeled data in this domain.

Tree-based data structures are commonly used by computational linguists for the documentation and analysis of morphological and syntactic data. In this paper we apply such structures to phonological data and demonstrate how such representations can have practical and beneficial applications in computational lexicography. To this end, we describe three integrated modules: the first defines a multilingual feature set within a tree-based structure using XML; the second module traverses this tree and generalises over the data contained within it, optimising the phonological data and highlighting feature implications. The third uses the information contained within the tree representation as a knowledge base for the generation of multiple feature-based syllable lexica.