Forecasting Application Counts in Talent Acquisition Platforms: Harnessing Multimodal Signals using LMs

As recruitment and talent acquisition have become more and more competitive, recruitment firms have become more sophisticated in using machine learning (ML) methodologies for optimizing their day to day activities. But, most of published ML based methodologies in this area have been limited to the tasks like candidate matching, job to skill matching, job classification and normalization. In this work, we discuss a novel task in the recruitment domain, namely, application count forecasting, motivation of which comes from designing of effective outreach activities to attract qualified applicants. We show that existing auto-regressive based time series forecasting methods perform poorly for this task. Henceforth, we propose a multimodal LM-based model which fuses job-posting metadata of various modalities through a simple encoder. Experiments from large real-life datasets from CareerBuilder LLC show the effectiveness of the proposed method over existing state-of-the-art methods.

Progress Notes Classification and Keyword Extraction using Attention-based Deep Learning Models with BERT

Various deep learning algorithms have been developed to analyze different types of clinical data including clinical text classification and extracting information from 'free text' and so on. However, automate the keyword extraction from the clinical notes is still challenging. The challenges include dealing with noisy clinical notes which contain various abbreviations, possible typos, and unstructured sentences. The objective of this research is to investigate the attention-based deep learning models to classify the de-identified clinical progress notes extracted from a real-world EHR system. The attention-based deep learning models can be used to interpret the models and understand the critical words that drive the correct or incorrect classification of the clinical progress notes. The attention-based models in this research are capable of presenting the human interpretable text classification models. The results show that the fine-tuned BERT with the attention layer can achieve a high classification accuracy of 97.6%, which is higher than the baseline fine-tuned BERT classification model. In this research, we also demonstrate that the attention-based models can identify relevant keywords that are strongly related to the clinical progress note categories.