Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userTopic Modeling for Maternal Health Using Reddit
نمذجة موضوع لصحة الأم باستخدام Reddit
290
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
تطبق هذه الورقة نمذجة الموضوع لفهم موضوعات صحة الأم والاهتمامات والأسئلة المعبرين عنها في المجتمعات عبر الإنترنت على مواقع الشبكات الاجتماعية.ندرس تحليل Dirichlet الكامن (LDA) وطريقين حديثين: نموذج موضوع عصبي مع تقطير المعرفة (KD) ونموذج الموضوع المدمج (ETM) على نصوص صحة الأم يتم جمعها من Reddit.يتم تقييم النماذج على جودة موضوع الاستدلال والموضوع، باستخدام مقاييس التقييم التلقائي والتقييم البشري.نحن نحلل قطع اتصال بين المقاييس التلقائية والتقييمات البشرية.في حين أن LDA يؤدي الأفضل بشكل عام مع مقاييس التقييم التلقائي NPMI والتماسك، فإن نموذج الموضوع العصبي مع تقطير المعرفة مواتية من خلال تقييم الخبراء.ونحن أيضا إنشاء خبير جديد جزئيا مشروح موضوع صحة الأم
This paper applies topic modeling to understand maternal health topics, concerns, and questions expressed in online communities on social networking sites. We examine Latent Dirichlet Analysis (LDA) and two state-of-the-art methods: neural topic model with knowledge distillation (KD) and Embedded Topic Model (ETM) on maternal health texts collected from Reddit. The models are evaluated on topic quality and topic inference, using both auto-evaluation metrics and human assessment. We analyze a disconnect between automatic metrics and human evaluations. While LDA performs the best overall with the auto-evaluation metrics NPMI and Coherence, Neural Topic Model with Knowledge Distillation is favorable by expert evaluation. We also create a new partially expert annotated gold-standard maternal health topic
References used
https://aclanthology.org/
rate research
Read More
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents. This work presents a
novel neural topic modeling framework using multi-view embed ding spaces: (1) pretrained topic-embeddings, and (2) pretrained word-embeddings (context-insensitive from Glove and context-sensitive from BERT models) jointly from one or many sources to improve topic quality and better deal with polysemy. In doing so, we first build respective pools of pretrained topic (i.e., TopicPool) and word embeddings (i.e., WordPool). We then identify one or more relevant source domain(s) and transfer knowledge to guide meaningful learning in the sparse target domain. Within neural topic modeling, we quantify the quality of topics and document representations via generalization (perplexity), interpretability (topic coherence) and information retrieval (IR) using short-text, long-text, small and large document collections from news and medical domains. Introducing the multi-source multi-view embedding spaces, we have shown state-of-the-art neural topic modeling using 6 source (high-resource) and 5 target (low-resource) corpora.
Personality and demographics are important variables in social sciences and computational sociolinguistics. However, datasets with both personality and demographic labels are scarce. To address this, we present PANDORA, the first dataset of Reddit co
mments of 10k users partially labeled with three personality models and demographics (age, gender, and location), including 1.6k users labeled with the well-established Big 5 personality model. We showcase the usefulness of this dataset on three experiments, where we leverage the more readily available data from other personality models to predict the Big 5 traits, analyze gender classification biases arising from psycho-demographic variables, and carry out a confirmatory and exploratory analysis based on psychological theories. Finally, we present benchmark prediction models for all personality and demographic variables.
We have introduced a new applications for Dynamic Factor Graphs, consisting in topic modeling, text classification and information retrieval. DFGs are tailored here to sequences of time-stamped documents.
Based on the auto-encoder architecture, our
nonlinear multi-layer model is trained stage-wise to produce increasingly more compact representations of bags-ofwords at the document or paragraph level, thus performing a semantic analysis. It also incorporates simple temporal dynamics on the latent representations, to take
advantage of the inherent (hierarchical) structure of sequences of documents, and can simultaneously perform a supervised classification or regression on document labels, which makes our approach unique. Learning this model is done by maximizing the joint likelihood of the encoding, decoding, dynamical and
supervised modules, and is possible using an approximate and gradient-based maximum-a-posteriori inference.
We demonstrate that by minimizing a weighted cross-entropy loss between his tograms of word occurrences and their reconstruction, we directly minimize the topic model
perplexity, and show that our topic model obtains lower perplexity than the Latent Dirichlet Allocation on the NIPS and State of the Union datasets. We illustrate how the dynamical constraints help the learning while enabling to visualize the topic trajectory.
Building maintenance is gaining an increasing attention in the field of scientific
research and there was a need for the use of new technologies in maintenance
management, , as the facility management deal with a large amount of information relatin
g
to maintenance, which includes drawings and specifications, lists, and reports, and the use
of paper files leads to decrease the efficiency and create a shortage of data and information.
We offer in this research tool to manage the maintenance process by using the BIM
through linking deterioration models and three-dimensional model of the facility to be
maintained, then compensate the external investigation data for maintenance work in these
models, to get at the end of this research and with the help of the possibilities offered by
Revit database to an upcoming maintenance items and arrange them according to their
importance and impact on the facility , the estimated budget for the periodic maintenance
and distribution of this budget on the various construction elements , the development of
resources plan for next year and up to the knowledge database of maintenance will help in
determining the supplier lifecycle and the coefficient of replacement.
Skin-to-skin contact (SSC) between the mother and her infant immediately postdelivery
is an important procedure that must be included in the care given to mother and
her infant many health benefits. The mother's desire and reaction towards (SSC) is
the
decisive factor in the success of this procedure, so the current study aimed to investigate
the initial reaction of 200 women towards (SSC) immediately after their vaginal delivery
were randomly selected from the obstetric hospital and national children in Lattakia, data
were collected using a questionnaire developed for this purpose. The study showed that
nearly three-quarters of mothers agreed to have contact with their children in (SSC) and
nearly three-quarters of approvals preferred to be covered. The highest rate of response to
approvals for contact with their children immediately after birth was the expression of joy
in child and then hugging and kissing him. While the highest response of non-approvals for
contact with their children immediately Post-Delivery refused to touch the child because
they were suffering of pain. The study recommended that every mother in the labor room
be investigated for carrying her naked child or swaddled with a blanket immediately after
birth, not forcing them to immediately postpartum (SSC), the study recommended to
continuing education and training sessions to provide all midwives and nurses working in
the labor rooms with information and skills (SSC) for all mothers immediately Post-
Delivery, and further research on nurse and midwife information on the importance of
prompt (SSC) immediately Post-Delivery.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
