Related papers: From a Word-Level Dictionary to Sentence-Level Semantics: Multilingual Grievance Labelling with Contextual Models

From a Word-Level Dictionary to Sentence-Level Semantics: Multilingual Grievance Labelling with Contextual Models

URL: http://arxiv.org/abs/2607.20946v1
Date: Thu, 23 Jul 2026 05:56:04 GMT
Title: From a Word-Level Dictionary to Sentence-Level Semantics: Multilingual Grievance Labelling with Contextual Models
Abstract summary: Grievance is one of the warning signs analysts look for when assessing threats of violence.<n>It is increasingly measured at scale from online text, most often with word-level lexicons that score by matching weighted terms.<n>We show that grievance is measured more faithfully by reading the surrounding context.
Score: 3.5005774502836995
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Grievance is one of the warning signs analysts look for when assessing threats of violence. It is increasingly measured at scale from online text, most often with word-level lexicons like the Grievance Dictionary that score by matching weighted terms. Such matching is a fast and transparent proxy, but it cannot resolve whether a term is asserted, quoted, negated, or condemned. These lexicons are also often evaluated on pools enriched with the very examples they retrieve, so a high score partly reflects agreement with the lexicon's own selection rule. Examining a five-language, 2{,}000-item evaluation pool, we find its halves separated almost perfectly by the lexicon itself: every item labeled ``random'' is in fact lexicon-negative, so the lexicon's apparent macro-AUROC of 0.686 collapses to a 0.500 floor fixed by construction. We keep the dictionary's 22-construct ontology but replace term matching with context-reading models, evaluated on a non-circular benchmark that separates unconditional-random, lexicon-positive, and lexicon-negative strata across five languages. Reading the full post rather than the target sentence alone helps most where the lexicon is silent, raising average precision on lexicon-negative text from 0.14 to 0.20, with the largest gains on quoted, implicit, and cross-sentence grievance. Together, these results show that grievance is measured more faithfully by reading the surrounding context, and more honestly when tested on text the lexicon did not select. We release our code and benchmark at https://github.com/behavioral-ds/multilingual_grievance.

Related papers

Measuring Alignment With Reader Highlights Net of Position and Length [1.8620637029128544]
On 120 web documents (at least 12 independent readers each), a language-model importance ranking keeps 38.4% of crowd-marked sentences against 19.9% of their matched neighbours.<n>A single human reader reaches +0.182 - indistinguishable from GPT-5.4 (+0.002 [-0.081, +0.088]) and below 5.
arXiv Detail & Related papers (2026-07-30T06:24:38Z)
When Certainty Is an Artifact: Keyword Lexicon Blindness and the (Mis)Measurement of Rhetorical Stance [4.093009863847175]
We find a robust negative-affect/emphatic-certainty lexical co-occurrence pattern under keyword-based scoring.<n>We argue that keyword lexicons measure a universal lexical co-occurrence tendency.
arXiv Detail & Related papers (2026-06-24T17:36:39Z)
Weave of Formal Thought [51.56484100374058]
We introduce Weave of Formal Thought (WoFT), a paradigm uniting rigorous syntactic validation with learned structural representations.<n>We present a latent-variable fine-tuning method training the language model to interleave non-terminal grammar symbols directly into generation.<n>For Python, fine-tuning StarCoder2-3B with our RWS objective reduces per-token cross-entropy by 14.3% relative to a text-only SFT baseline.
arXiv Detail & Related papers (2026-06-24T15:58:11Z)
A Typologically Grounded Evaluation Framework for Word Order and Morphology Sensitivity in Multilingual Masked LMs [2.895343274331944]
We evaluate mBERT and XLM-R on English, Chinese, German, Spanish, and Russian languages.<n>Full scrambling drives word-level reconstruction accuracy near zero in all languages.<n>Top-5 word accuracy shows the same pattern: under full scrambling, the gold word rarely appears among the five highest-ranked reconstructions.
arXiv Detail & Related papers (2026-02-28T03:13:34Z)
Levée d'ambiguïtés par grammaires locales [0.0]
This article concerns a lexical disambiguation method adapted to the objective of a zero silence rate and implemented in Silberztein's INTEX system (1993).<n>We show that to verify a local disambiguation grammar in this framework, it is not sufficient to consider the transducer paths separately.
arXiv Detail & Related papers (2025-10-28T15:38:22Z)
Provably Secure Disambiguating Neural Linguistic Steganography [66.30965740387047]
The segmentation ambiguity problem, which arises when using language models based on subwords, leads to occasional decoding failures.<n>We propose a novel secure disambiguation method named SyncPool, which effectively addresses the segmentation ambiguity problem.<n> SyncPool does not change the size of the candidate pool or the distribution of tokens and thus is applicable to provably secure language steganography methods.
arXiv Detail & Related papers (2024-03-26T09:25:57Z)
Understanding and Mitigating Spurious Correlations in Text Classification with Neighborhood Analysis [69.07674653828565]
Machine learning models have a tendency to leverage spurious correlations that exist in the training set but may not hold true in general circumstances. In this paper, we examine the implications of spurious correlations through a novel perspective called neighborhood analysis. We propose a family of regularization methods, NFL (doN't Forget your Language) to mitigate spurious correlations in text classification.
arXiv Detail & Related papers (2023-05-23T03:55:50Z)
Lex2Sent: A bagging approach to unsupervised sentiment analysis [0.628122931748758]
In this paper, we propose an alternative approach to classifying texts: Lex2Sent. To classify texts, we train embedding models to determine the distances between document embeddings and the embeddings of a suitable lexicon. We show that our model outperforms lexica and provides a basis for a high performing few-shot fine-tuning approach in the task of binary sentiment analysis.
arXiv Detail & Related papers (2022-09-26T20:49:18Z)
DP-Parse: Finding Word Boundaries from Raw Speech with an Instance Lexicon [18.05179713472479]
We introduce DP-Parse, which uses similar principles but only relies on an instance lexicon of word tokens. On the Zero Resource Speech Benchmark 2017, our model sets a new speech segmentation state-of-the-art in 5 languages. Despite lacking a type lexicon, DP-Parse can be pipelined to a language model and learn and semantic representations as assessed by a new spoken word embedding benchmark.
arXiv Detail & Related papers (2022-06-22T19:15:57Z)
Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment [49.3253280592705]
We show it is possible to produce much higher quality lexicons with methods that combine bitext mining and unsupervised word alignment. Our final model outperforms the state of the art on the BUCC 2020 shared task by 14 $F_1$ points averaged over 12 language pairs.
arXiv Detail & Related papers (2021-01-01T03:12:42Z)
Speakers Fill Lexical Semantic Gaps with Context [65.08205006886591]
We operationalise the lexical ambiguity of a word as the entropy of meanings it can take. We find significant correlations between our estimate of ambiguity and the number of synonyms a word has in WordNet. This suggests that, in the presence of ambiguity, speakers compensate by making contexts more informative.
arXiv Detail & Related papers (2020-10-05T17:19:10Z)
Phonotactic Complexity and its Trade-offs [73.10961848460613]
This simple measure allows us to compare the entropy across languages. We demonstrate a very strong negative correlation of -0.74 between bits per phoneme and the average length of words.
arXiv Detail & Related papers (2020-05-07T21:36:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.