Grammatical gender assignment is governed by interacting phonological, semantic, and morphological factors whose relative weight varies across languages. We present the first large-scale computational analysis of gender in Hindi using a novel dataset of over 6,000 noun paradigms annotated for morphological, phonological, semantic, and lexical strata features. We tested inflection class against competing predictors with interpretable classifiers and neural networks and found that it dominates, predicting gender at ≈97% accuracy, while other features contribute negligibly. The dependence is asymmetric: inflection class nearly determines gender, whereas gender is only a partial predictor of inflection class. Inflection class is itself recoverable in part from phonological and etymological cues without reference to gender, which suggests a resolution to the bootstrapping problem: form-based cues scaffold inflection class, which in turn scaffolds gender. These findings establish Hindi as an extreme case of morphologically aligned gender and expose a limitation of language-agnostic approaches: methods that treat semantic and surface-form cues as broadly predictive of gender may miss Hindi’s near-deterministic dependence on inflection class. Our dataset enables probing whether multilingual models encode this dependency and whether it survives cross-lingual transfer.

This paper presents guidelines for the annotation of deliberate linguistic metaphor. Expressions that contribute to the same metaphorical image are annotated as a chain along with a semantically contrasting expression of the target domain, which helps to make the domain contrast inherent to metaphor more explicit. So far, a corpus of ten TEDx talks with a total of ca. 20k tokens has been annotated according to these guidelines. 1.35% of the tokens are deliberate metaphorical expressions according to our guidelines, which shows that our guidelines successfully identify a significantly higher proportion of deliberate metaphorical expressions than previous studies.

We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or “inked”) notes on a virtual whiteboard. Our approach allows us to create a single, low-latency model to simultaneously perform sentence segmentation and classification of these sentences into task/non-task sentences. SLATE greatly outperforms a baseline two-model (sentence segmentation followed by classification model) approach, achieving a task F1 score of 84.4%, a sentence segmentation (boundary similarity) score of 88.4% and three times lower latency compared to the baseline. Furthermore, we provide insights into tackling challenges of performing NLP on the inking domain. We release both our code and dataset for this novel task.