Men Are from Mars, Women Are from Venus: Evaluation and Modelling of Verbal Associations

We present a quantitative analysis of human word association pairs and study the types of relations presented in the associations. We put our main focus on the correlation between response types and respondent characteristics such as occupation and gender by contrasting syntagmatic and paradigmatic associations. Finally, we propose a personalised distributed word association model and show the importance of incorporating demographic factors into the models commonly used in natural language processing.Comment: AIST 2017 camera-read

Corpus study of the choice of personal pronouns in social media chats among tertiary students

Purpose: Sociolinguistics variables such as age, gender and social class, among others, are said to bring the difference in the ways humans communicate. In this essence, this study investigated how age brings out the difference in the use of pronouns between three generational groupsDesign/Methodology/Approach: Group WhatsApp chats were collected from three generational groups of students based on their willingness to give it out for analysis. These three generational groups are postgraduate students and undergraduates (Level 100, Level 400). These groups were randomly collected from over four hundred (400 students of the University of Ghana and the University of Cape Coast in Ghana based on convenience. A quantitative research design was adopted for this study with the help of a corpus tool (AntConc) to analyse the huge data gathered based on percentages.Findings: This study found out that postgraduates and undergraduates (Level 100) use the first-person singular pronoun but postgraduates, especially those who represent the older generation tend to use the first-person pronoun more often while the second undergraduate group members (Level 400) use the third person pronouns. The study, per the findings, concludes that people tend to affiliate with others when they are young and lose group affiliation as they grow.Research Limitation: The study was limited to WhatsApp group chats of university students within the undergraduate and postgraduate academic levels who permitted their chats to be used. This resulted in a narrow scope for the groups used.Practical implication: The study reveals the preference or choice of pronoun usage among people based on their ages and the groups they affiliate more with. People may thus become more conscious of the choice they make of pronouns for usage as they become older or affiliate with older people.Social Implications: the study re-examines existing literature on the use of adverbs, especially personal pronouns. Since many studies have been done on the use of adverbs, this adds a new strand of knowledge to existing ones on the subject.Originality/Value: It provided empirical data on the demographic, precisely age characteristics of the subjects used for the research that affects their choice of personal pronouns among themselves and others

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information via recommendations. Prior work obfuscates user-item data before sharing it with recommendation system. This approach does not explicitly address the quality of recommendation while performing data obfuscation. Moreover, it cannot protect users against private-attribute inference attacks based on recommendations. This work is the first attempt to build a Recommendation with Attribute Protection (RAP) model which simultaneously recommends relevant items and counters private-attribute inference attacks. The key idea of our approach is to formulate this problem as an adversarial learning problem with two main components: the private attribute inference attacker, and the Bayesian personalized recommender. The attacker seeks to infer users' private-attribute information according to their items list and recommendations. The recommender aims to extract users' interests while employing the attacker to regularize the recommendation process. Experiments show that the proposed model both preserves the quality of recommendation service and protects users against private-attribute inference attacks.Comment: The Thirteenth ACM International Conference on Web Search and Data Mining (WSDM 2020

Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers

Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior belief, we show that generative models alone are not sufficient to prevent shortcut learning, despite an incentive to recover a more comprehensive representation of the data than discriminative approaches. However, we observe that shortcuts are preferentially encoded with minimal information, a fact that generative models can exploit to mitigate shortcut learning. In particular, we propose Chroma-VAE, a two-pronged approach where a VAE classifier is initially trained to isolate the shortcut in a small latent subspace, allowing a secondary classifier to be trained on the complementary, shortcut-free latent subspace. In addition to demonstrating the efficacy of Chroma-VAE on benchmark and real-world shortcut learning tasks, our work highlights the potential for manipulating the latent space of generative classifiers to isolate or interpret specific correlations.Comment: Presented at the 36th Conference on Neural Information Processing Systems (NeurIPS 2022

How we do things with words: Analyzing text as social and cultural data

In this article we describe our experiences with computational text analysis. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of best practices for working with thick social and cultural concepts. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that will resonate for many. And this leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis that involves social and cultural concepts, and the more we are able to bridge these divides, the more fruitful we believe our work will be