This paper presents a pilot project named Cancer FrameNet. The project's goal is a general-purpose natural language processing (NLP) resource for cancer-related information in clinical notes (i.e., patient records in an electronic health record system). While previous cancer NLP annotation projects have largely been ad hoc resources to address a specific and immediate information need, the frame semantic method employed here emphasizes the information presented in the notes themselves and its linguistic structure. To this end, three semantic frames (targeting the high-level tasks of cancer diagnoses, cancer therapeutic procedures, and tumor descriptions) are created and annotated on a clinical text corpus. Prior to annotation, candidate sentences are extracted from a clinical data warehouse and de-identified to remove any private information. The frames are then annotated with the three frames totaling over thirty frame elements. This paper describes these steps in the pilot project and discusses issues encountered to evaluate the feasibility of general-purpose linguistic resources for extracting cancer-related information.
@InProceedings{ROBERTS18.37, author = {Kirk Roberts and Yuqi Si and Anshul Gandhi and Elmer Bernstam}, title = "{A FrameNet for Cancer Information in Clinical Narratives: Schema and Annotation}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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