The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents. Previous research has highlighted Positive and Unlabeled (PU) learning as a promising approach for this task. However, most PU methods rely on the unrealistic assumption of knowing the class prior for positive samples in the collection. To address this limitation, this paper introduces a novel PU learning framework that utilizes intractable density estimation models. Experiments conducted on PubMed and Covid datasets in a transductive setting showcase the effectiveness of the proposed method for DSE. Code is available from https://github.com/Beautifuldog01/Document-set-expansion-puDE.

Accurate prediction of stock prices is considered as a significant practical challenge and has been a longstanding topic of debate within the economic domain. In recent years, sentiment analysis on social media comments has been considered an important data source for stock prediction. However, most of these works focus on exploring stocks with high market values or from specific industries. The extent to which sentiments affect a broader range of stocks and their overall performance remains uncertain. In this paper, we study the influence of sentiment analysis on stock price prediction with respect to (1) different market value groups and (2) different Book-to-Market ratio groups in the Chinese stock market. To this end, we create a new dataset that consists of 24 stocks across different market value groups and Book-to-Market ratio categories, along with 12,000 associated comments that have been collected and manually annotated. We then utilized this dataset to train a variety of sentiment classifiers, which were subsequently integrated into sequential neural-based models for stock price prediction. Experimental findings indicate that while sentiment integration generally improve the predictive performance for price prediction, it may not consistently lead to better results for individual stocks. Moreover, these outcomes are notably influenced by varying market values and Book-to-Market ratios, with stocks of higher market values and B/M ratios often exhibiting more accurate predictions. Among all the models tested, the Bi-LSTM model incorporated with the sentiment analysis, achieves the best prediction performance.

It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pairs to measure bias and extract biased analogies. We show that the reliance on these gendered pairs has strong limitations: bias measures based off of them are not robust and cannot identify common types of real-world bias, whilst analogies utilising them are unsuitable indicators of bias. In particular, the well-known analogy “man is to computer-programmer as woman is to homemaker” is due to word similarity rather than bias. This has important implications for work on measuring bias in embeddings and related work debiasing embeddings.