Human values are enduring motivational principles that shape how people argue, justify, and evaluate social issues. While prior work has largely approached value detection as a standard classification problem, we investigate what values look like linguistically and whether computational models reflect aspects of the structure proposed by Schwartz’s theory of basic human values. Using the training partition of the Touché ValueEval corpus, we compare lexical resources, linguistic markers, supervised classifiers over TF–IDF and sentence embeddings, an automated model-search procedure, a zero-shot large language model (Gemini 3.5 Flash), and a transformer embedding baseline (all-MiniLM-L6-v2). On a shared 124-item test split, the three dense semantic approaches form a statistically indistinguishable high-performing tier (macro-F1 0.529–0.591) and achieve substantially higher scores than the sparse lexical methods, highlighting the importance of contextual semantic representation in this setting. A constructed diagnostic sanity check shows that deontic modals, universal quantifiers, and abstract nouns behave in the expected direction for value-bearing versus opinion language; however, these cues provide little information for distinguishing individual values in the Touché data. Performance is less stable for low-support value categories. Qualitative inspection of model confusions reveals several patterns consistent with theoretically close Schwartz values, alongside confusions between more distant categories; circumplex adjacency is not formally tested. Overall, the results suggest a distinction between surface linguistic cues associated with value-bearing language and the contextual semantic information involved in distinguishing specific values, while offering qualitative evidence that some model confusions align with relationships in Schwartz’s motivational structure.

Citation identification in historical and ancient texts poses challenges that extend beyond surface-level pattern recognition, including implicit references, morphological fusion, and discourse-driven ambiguity. In this work, we address citation Named Entity Recognition (NER) in medieval Hebrew Responsa literature using a modular, LLM-based correction pipeline. Rather than treating large language models as end-to-end predictors, we leverage them as structured components: an initial prompt-based expert tagger, complementary LLM judges for systematic error detection, and domain-aware correction grounded in philological regularities. Our approach requires no end-to-end fine-tuning and only minimal labeled supervision (a small validation set for training a lightweight error-detection classifier), narrowing the performance gap to strong supervised models trained on domain-specific data. The results suggest that explicit error handling and interpretability-driven design offer a promising direction for historical NLP in low-resource settings.

Equity crowdfunding under U.S. Regulation Crowdfunding (Reg CF) raised over 1.4 billion across 5,969 offerings between May 2016 and December 2022 (Chang, 2024), generating a large public corpus of Form C business descriptions on SEC EDGAR. Yet regulated equity crowdfunding remains barely studied by the financial NLP community. We conduct a systematic mapping review using automated database search, independent dual-LLM screening, targeted human adjudication, and blind quality-control auditing. We screened 726 deduplicated papers and included 49 studies applying NLP directly to crowdfunding campaign text. Only one study examines U.S. Reg CF campaign text directly, using readability, with one pre-Reg-CF Title II near-neighbor using sentiment tone and description length. The available equity-crowdfunding evidence is therefore too limited and heterogeneous to support general conclusions about the strength of textual signals in regulated equity settings. We taxonomize the included studies into five NLP method families, synthesize representative cross-study evidence in the original reported units, and identify the near absence of regulated equity research across the methodological spectrum. We also discuss regulatory constraints, structured financial information, and text length as plausible but untested explanations for this gap. The annotated evidence base, screening records, and reproducibility materials are publicly available at https://github.com/jct-nlp/finNLP-crowdfunding

In this paper, we present our contribution in SemEval-2021 Task 1: Lexical Complexity Prediction, where we integrate linguistic, statistical, and semantic properties of the target word and its context as features within a Machine Learning (ML) framework for predicting lexical complexity. In particular, we use BERT contextualized word embeddings to represent the semantic meaning of the target word and its context. We participated in the sub-task of predicting the complexity score of single words

Aramaic is an ancient Semitic language with a 3,000 year history. However, since the number of Aramaic speakers in the world hasdeclined, Aramaic is in danger of extinction. In this paper, we suggest a methodology for automatic construction of Aramaic-Hebrew translation Lexicon. First, we generate an initial translation lexicon by a state-of-the-art word alignment translation model. Then,we filter the initial lexicon using string similarity measures of three types: similarity between terms in the target language, similarity between a source and a target term, and similarity between terms in the source language. In our experiments, we use a parallel corporaof Biblical Aramaic-Hebrew sentence pairs and evaluate various string similarity measures for each type of similarity. We illustratethe empirical benefit of our methodology and its effect on precision and F1. In particular, we demonstrate that our filtering methodsignificantly exceeds a filtering approach based on the probability scores given by a state-of-the-art word alignment translation model.