Three levels of framing.

A sociologist and a linguist, unaware of each other's work, each assigned a technical meaning to the term frame around 1970, based on separate usages of the word frame from the 1950s. Each researcher instigated a theory of frame analysis. Over the following decades, the two approaches to framing became intertwined as followers of both Goffman and Fillmore studied metaphoric framing, examined factors affecting the communication of frames, and became particularly interested in politics and the mass media. Years later, many theorists complain about the fragmented field of frame studies. The paper suggests that some of the fragmentation can be resolved by recognizing the dual origins of framing studies, and classifying instances of framing in either the Goffman or the Fillmore tradition as occurring at the level of language, thought, or communication. These three levels are termed semantic framing, cognitive framing, and communicative framing. This article is categorized under: Linguistics > Cognitive Linguistics > Linguistic Theory Computer Science and Robotics > Natural Language Processing.

"The very interesting finding suggests that ": A cognitive frame-based analysis of interest markers by authors' geo-academic location in applied linguistics research articles.

Linguistic expressions of interest instantiated by interesting, intriguing, and fascinating that signal the authorial stance are not uncommon in applied linguistics research articles. Nevertheless, they have received little scholarly attention. This paper, taking a cognitive semantic approach, reports on a study that sought to examine how linguistically expressed interest in applied linguistics research articles is leveraged by researchers' geo-academic location (the Core vs. the Periphery). Drawing on a semantic frame generated for interest markers in academic writing, this study focused on the incidence of the various elements of the Interest frame in the discipline of applied linguistics based on a mixed-methods approach. The corpus-based quantitative analyses found that academic writers' geo-academic location was a robust predictor of authors' overall use of interest markers and some frame elements associated with the Interest frame. Triangulation with the interview data obtained from disciplinary specialists revealed that the observed differences could be attributable to the hierarchical academia featuring periphery-based scholars' unequal access to the knowledge production market and under-representation.

Embodied Metarepresentations.

Meaning has been established pervasively as a central concept throughout disciplines that were involved in cognitive revolution. Its metaphoric usage comes to be, first and foremost, through the interpreter's constraint: representational relationships and contents are considered to be in the "eye" or mind of the observer and shared properties among observers themselves are knowable through interlinguistic phenomena, such as translation. Despite the instability of meaning in relation to its underdetermination by reference, it can be a tertium comparationis or "third comparator" for extended human cognition if gauged through invariants that exist in transfer processes such as translation, as all languages and cultures are rooted in pan-human experience and, thus, share and express species-specific ontology. Meaning, seen as a cognitive competence, does not stop outside of the body but extends, depends, and partners with other agents and the environment. A novel approach for exploring the transfer properties of some constituent items of the original natural semantic metalanguage in English, that is, semantic primitives, is presented: FrameNet's semantic frames, evoked by the primes SEE and FEEL, were extracted from EuroParl, a parallel corpus that allows for the automatic word alignment of items with their synonyms. Large Ontology Multilingual Extraction was used. Afterward, following the Semantic Mirrors Method, a procedure that consists back-translating into source language, a translatological examination of translated and original versions of items was performed. A fully automated pipeline was designed and tested, with the purpose of exploring associated frame shifts and, thus, beginning a research agenda on their alleged universality as linguistic features of translation, which will be complemented with and contrasted against further massive feedback through a citizen science approach, as well as cognitive and neurophysiological examinations. Additionally, an embodied account of frame semantics is proposed.

Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports.

This paper proposes a representation framework for encoding spatial language in radiology based on frame semantics. The framework is adopted from the existing SpatialNet representation in the general domain with the aim to generate more accurate representations of spatial language used by radiologists. We describe Rad-SpatialNet in detail along with illustrating the importance of incorporating domain knowledge in understanding the varied linguistic expressions involved in different radiological spatial relations. This work also constructs a corpus of 400 radiology reports of three examination types (chest X-rays, brain MRIs, and babygrams) annotated with fine-grained contextual information according to this schema. Spatial trigger expressions and elements corresponding to a spatial frame are annotated. We apply BERT-based models (BERTBASE and BERTLARGE) to first extract the trigger terms (lexical units for a spatial frame) and then to identify the related frame elements. The results of BERTLARGE are decent, with F1 of 77.89 for spatial trigger extraction and an overall F1 of 81.61 and 66.25 across all frame elements using gold and predicted spatial triggers respectively. This frame-based resource can be used to develop and evaluate more advanced natural language processing (NLP) methods for extracting fine-grained spatial information from radiology text in the future.

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.

OBJECTIVE: There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. METHODS: We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 78 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps. RESULTS: Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 78), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. CONCLUSION: The list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.