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1. Progressive Sentiment Analysis for Code-Switched Text Data

   Ranjan, Sudhanshu; Mekala, Dheeraj; Shang, Jingbo (January 2022, Findings of the Association for Computational Linguistics: EMNLP 2022)

   Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition.However, similar methods for transfer learning from monolingual text to code-switched text have not been extensively explored mainly due to the following challenges:(1) Code-switched corpus, unlike monolingual corpus, consists of more than one language and existing methods can’t be applied efficiently,(2) Code-switched corpus is usually made of resource-rich and low-resource languages and upon using multilingual pre-trained language models, the final model might bias towards resource-rich language. In this paper, we focus on code-switched sentiment analysis where we have a labelled resource-rich language dataset and unlabelled code-switched data. We propose a framework that takes the distinction between resource-rich and low-resource language into account.Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples. Extensive experiments across multiple language pairs demonstrate that progressive training helps low-resource language dominated samples. 

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2. A Digital Corpus of St. Lawrence Island Yupik

   Schwartz, Lane; Chen, Emily; Park, Hyunji Hayley; Jahn, Edward; Schreiner, Sylvia L.R. (March 2021, Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages (ComputEL-4))

   null (Ed.)

   St. Lawrence Island Yupik (ISO 639-3: ess) is an endangered polysynthetic language in the Inuit-Yupik language family indigenous to Alaska and Chukotka. This work presents a step-by-step pipeline for the digitization of written texts, and the first publicly available digital corpus for St. Lawrence Island Yupik, created using that pipeline. This corpus has great potential for future linguistic inquiry and research in NLP. It was also developed for use in Yupik language education and revitalization, with a primary goal of enabling easy access to Yupik texts by educators and by members of the Yupik community. A secondary goal is to support development of language technology such as spell-checkers, text-completion systems, interactive e-books, and language learning apps for use by the Yupik community. 

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3. Moral Foundations Twitter Corpus: A Collection of 35k Tweets Annotated for Moral Sentiment

   https://doi.org/10.1177/1948550619876629  

   Hoover, Joe; Portillo-Wightman, Gwenyth; Yeh, Leigh; Havaldar, Shreya; Davani, Aida Mostafazadeh; Lin, Ying; Kennedy, Brendan; Atari, Mohammad; Kamel, Zahra; Mendlen, Madelyn; et al (February 2020, Social Psychological and Personality Science)

   Research has shown that accounting for moral sentiment in natural language can yield insight into a variety of on- and off-line phenomena such as message diffusion, protest dynamics, and social distancing. However, measuring moral sentiment in natural language is challenging, and the difficulty of this task is exacerbated by the limited availability of annotated data. To address this issue, we introduce the Moral Foundations Twitter Corpus, a collection of 35,108 tweets that have been curated from seven distinct domains of discourse and hand annotated by at least three trained annotators for 10 categories of moral sentiment. To facilitate investigations of annotator response dynamics, we also provide psychological and demographic metadata for each annotator. Finally, we report moral sentiment classification baselines for this corpus using a range of popular methodologies. 

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4. Bias in Text Analysis for International Relations Research

   https://doi.org/10.1093/isagsq/ksac021  

   Windsor, Leah C (May 2022, Global Studies Quarterly)

   Abstract How international is political text-analysis research? In computational text analysis, corpus selection skews heavily toward English-language sources and reflects a Western bias that influences the scope, interpretation, and generalizability of research on international politics. For example, corpus selection bias can affect our understanding of alliances and alignments, internal dynamics of authoritarian regimes, durability of treaties, the onset of genocide, and the formation and dissolution of non-state actor groups. Yet, there are issues along the entire “value chain” of corpus production that affect research outcomes and the conclusions we draw about things in the world. I identify three issues in the data-generating process pertaining to discourse analysis of political phenomena: information deficiencies that lead to corpus selection and analysis bias; problems regarding document preparation, such as the availability and quality of corpora from non-English sources; and gaps in the linguist analysis pipeline. Short-term interventions for incentivizing this agenda include special journal issues, conference workshops, and mentoring and training students in international relations in this methodology. Longer term solutions to these issues include promoting multidisciplinary collaboration, training students in computational discourse methods, promoting foreign language proficiency, and co-authorship across global regions that may help scholars to learn more about global problems through primary documents. 

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5. Variable-coefficient parabolic theory as a high-dimensional limit of elliptic theory

   https://doi.org/10.1007/s00526-023-02644-x  

   Davey, Blair; Smit Vega Garcia, Mariana (January 2024, Calculus of Variations and Partial Differential Equations)

   Abstract This paper continues the study initiated in Davey (Arch Ration Mech Anal 228:159–196, 2018), where a high-dimensional limiting technique was developed and used to prove certain parabolic theorems from their elliptic counterparts. In this article, we extend these ideas to the variable-coefficient setting. This generalized technique is demonstrated through new proofs of three important theorems for variable-coefficient heat operators, one of which establishes a result that is, to the best of our knowledge, also new. Specifically, we give new proofs of$$L^2 \rightarrow L^2$$ $L^2\to L^2$Carleman estimates and the monotonicity of Almgren-type frequency functions, and we prove a new monotonicity of Alt–Caffarelli–Friedman-type functions. The proofs in this article rely only on their related elliptic theorems and a limiting argument. That is, each parabolic theorem is proved by taking a high-dimensional limit of a related elliptic result. 

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