Developing Lactation Support in a Primary Care Pediatric Office

Background: Breast milk is known to be the best nutrition for infants, and experts recommend to exclusively breastfeed through the first six months of life and provide breastmilk through at least the first year of a child’s life (American Academy of Pediatrics, 2012). The United States is currently not achieving its breastfeeding goals set out in Healthy People 2020 by the US Department of Health and Human Services (U.S. Department of Health and Human Services, 2014). The integration of Internationally Board-Certified Lactation Consultants (IBCLC) into primary care pediatrics offices has been shown to increase all breastfeeding rates (Witt, Smith, Mason, & Flocke, 2012). The purpose of this paper is to describe how the DNP student developed a business plan for the employment of a lactation counselor in a primary care pediatric office in West Michigan. Objectives: To introduce the role of lactation support in the primary care setting. This will be done by creating a business plan for the employment of an IBCLC in a primary care pediatric office and to develop a policy for outpatient lactation support for the organization. Methods: The lactation counselor will be integrated into a pediatric office which is part of a larger health care organization, in the midwestern United States. Breastfeeding mothers that are seen in the office were asked to participate in a pre-intervention survey about lactation support. Lewin’s Change Management Model is used to guide the project. Results: The pre-intervention survey was completed and a business plan for the organization has been produced. Conclusions: Preliminary data has been collected and has been used for the creation of a business plan for the healthcare organization

Kicktionary-LOME:A Domain-Specific Multilingual Frame Semantic Parsing Model for Football Language

This technical report introduces an adapted version of the LOME frame semantic parsing model (Xia et al., EACL 2021) which is capable of automatically annotating texts according to the "Kicktionary" domain-specific framenet resource. Several methods for training a model even with limited available training data are proposed. While there are some challenges for evaluation related to the nature of the available annotations, preliminary results are very promising, with the best model reaching F1-scores of 0.83 (frame prediction) and 0.81 (semantic role prediction)

The Latin Readers of Algazel, 1150-1600

This dissertation examines how Arabic works found an audience in medieval Europe and became a part of the Latin canon of philosophy. It focuses on a Latin translation of an Arabic philosophical work, Maqasid al-falasifa, by the Muslim theologian al-Ghazali, known as Algazel in Latin. This work became popular because it served as a primer for Arab philosophy and helped Latins understand a tradition that had built upon Greek scholarship for centuries. To find the translation’s audience, this project looks at two sets of evidence. It studies the works of Latin scholars who drew from Algazel’s arguments and illustrates that the translation’s influence was more extensive than historians have previously thought. It also examines copies of the translation in forty manuscripts and broadens the Latin audience of Arab philosophy beyond what historians typically study—the university—to include the anonymous scribes and readers who comprise the often-voiceless majority of medieval literate society. These codices yield details about Algazel’s readers, their interests and concerns, which cannot be gathered from other sources. Scholars spared little expense with these manuscripts since several are quite ornate or contain gold leaf. Many copies possess wide margins where scholars interacted with the text by writing notes, diagrams, pointing hands, warnings, and the occasional doodle. Scribes integrated the work into the established canon by placing Algazel in manuscripts with Christian philosophers from Augustine to Aquinas. The manuscripts also contain marginalia left by generations of readers, which give insight into how scholars read the text and what passages grabbed their attention. The notes indicate that a few readers agreed with ecclesiastical authorities who condemned Algazel’s work since some scholars wrote warnings in the margins alongside passages that they considered dangerous. Thus, Latins paradoxically expended great effort to understand Arab philosophers while simultaneously condemning ideas in the translations as errors. This study expands our understanding of the European interaction with the Arab tradition by examining reading practices with evidence drawn from the readers themselves. It demonstrates that Europeans read translated Arabic works alongside long-standing authorities and treated Arab authors as valuable members of the Latin canon

Towards a Dutch FrameNet lexicon and parser using the data-to-text method

Our presentation introduces the Dutch FrameNet project, whose major outcomes will be a FrameNet-based lexicon and semantic parser for Dutch. This project implements the ‘data-to-text’ method (Vossen et al., LREC 2018), which involves collecting structured data about specific types of real-world events, and then linking this to texts referring to these events. By contrast, earlier FrameNet projects started from text corpora without assumptions about the events they describe. As a consequence, these projects cover a wide variety of events and situations (‘frames’), but have a limited number of annotated examples for every frame. By starting from structured domains, we avoid this sparsity problem, facilitating both machine learning and qualitative analyses on texts in the domains we annotate. Moreover, the data-to-text approach allows us to study the three-way relationship between texts, structured data, and frames, highlighting how real-world events are ‘framed’ in texts. We will discuss the implications of using the data-to-text method for the design and theoretical framework of the Dutch FrameNet and for automatic parsing. First of all, a major departure from traditional frame semantics is that we can use structured data to enrich and inform our frame analyses. For example, certain frames have a strong conceptual link to specific events (e.g., a text cannot describe a murder event without evoking the Killing frame), but texts describing these events may evoke these frames in an implicit way (e.g., a murder described without explicitly using words like ‘kill’), which would lead these events to be missed by traditional FrameNet annotations. Moreover, we will investigate how texts refer to the structured data and how to model this in a useful way for annotators. We theorize that variation in descriptions of the real world is driven by pragmatic requirements (e.g., Gricean maxims; Weigand, 1998) and shared event knowledge. For instance, the sentence ‘Feyenoord hit the goal twice’ implies that Feyenoord scored two points, but this conclusion requires knowledge of Feyenoord and what football matches are like. We will present both an analysis of the influence of world knowledge and pragmatic factors on variation in lexical reference, and ways to model this variation in order to annotate references within and between texts concerning the same event. Automatic frame semantic parsing will adopt a multilingual approach: the data-to-text approach makes it relatively easy to gather a corpus of texts in different languages describing the same events. We aim to use techniques such as cross-lingual annotation projection (Evang & Bos, COLING 2016) to adapt existing parsers and resources developed for English to Dutch, our primary target language, but also to Italian, which will help us make FrameNet and semantic parsers based on it more language-independent. Our parsers will be integrated into the Parallel Meaning Bank project (Abzianidze et al., EACL 2017)

Towards Reference-Aware FrameNet Annotation

In this paper, we introduce the task of using FrameNet to link structured information about real-world events to the conceptual frames used in texts describing these events. We show that frames made relevant by the knowledge of the real-world event can be captured by complementing standard lexicon-driven FrameNet annotations with frame annotations derived through pragmatic inference. We propose a two-layered annotation scheme with a ‘strict’FrameNet-compatible lexical layer and a ‘loose’layer capturing frames that are inferred from referential data