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Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications.

In the past, people studied the stock market based on the assumption that the stock entity is known to be affected by the news. However, due to this assumption, these methods inevitably ignore the news without stock entities, and many news without stock entities will also have a significant impact on financial markets. In order to solve this problem, this paper proposes a subgraph matching algorithm based on semantic paths. Matching subgraphs on a knowledge graph that collects a large amount of stock market information and matching the affected stock entities from the semantic level can make a comprehensive analysis on Various news with or without entities. The main research work and achievements of this paper are as follows: First, starting with structured data, the paper complements semi-structured data and unstructured data to build a knowledge graph of the stock market and covering most of the stock market entities. Secondly, based on the analysis of LDA topic model, this dissertation extracts useful topics from financial news and constructs a news graph. A subgraph matching algorithm based on semantic path is proposed. From the knowledge graph, subgraphs matching with news graph are searched for mining the associated entities in the financial news. Finally, according to the result of subgraph matching, experiments and simulated investment are designed. The strategy achieved 15.96% excess return relative to the benchmark. The effectiveness of the subgraph matching algorithm based on semantic path is verified, and the feasibility of the algorithm in actual investment is proved.