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Harrisen Scells

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Information Retrieval Computation and Language Machine Learning

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preprint2026arXiv

Investigating Counterclaims in Causality Extraction from Text

Many causal claims, such as "sugar causes hyperactivity," are disputed or outdated. Yet research on causality extraction from text has almost entirely neglected counterclaims of causation. To close this gap, we conduct a thorough literature review of causality extraction, compile an extensive inventory of linguistic realizations of countercausal claims, and develop rigorous annotation guidelines that explicitly incorporate countercausal language. We also highlight how counterclaims of causation are an integral part of causal reasoning. Based on our guidelines, we construct a new dataset comprising 1028 causal claims, 952 counterclaims, and 1435 uncausal statements, achieving substantial inter-annotator agreement (Cohen's $κ= 0.74$). In our experiments, state-of-the-art models trained solely on causal claims misclassify counterclaims more than 10 times as often as models trained on our dataset.

preprint2026arXiv

Understanding Wacky Weights: A Dissection of SPLADE's Learned Term Importance

Learned sparse retrieval models such as SPLADE combine the effectiveness of neural architectures with the efficiency of inverted indices. As these models assign weights to terms from a fixed vocabulary, interpretability is often touted as a major benefit of these models. However, the emergence of wacky weights, i.e., expansion terms that appear semantically unrelated to the input, limits interpretability. While prior research has anecdotally observed this phenomenon, there is a lack of systematic understanding regarding their origins, prevalence, and contribution to retrieval effectiveness. In this paper, we reproduce SPLADE-v2 to systematically investigate wacky weights across the SPLADE family of models. We present a comprehensive dissection of wacky weights, providing a formal definition of wackiness based on the lexical utility of expansion terms. Furthermore, we introduce a novel measure to compare the prevalence of these tokens across models with varying vocabularies and sparsity levels. Beyond reproducing the original SPLADE-v2, we train it with various loss functions, datasets, and backbone transformers to isolate the factors contributing to wackiness. Our results show that larger vocabularies are associated with a higher prevalence of wacky tokens, while stricter sparsity regularizers are associated with lower prevalence. Finally, we find that wacky weights are used primarily for in-domain effectiveness rather than out-of-domain generalization.

preprint2022arXiv

From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search

Medical systematic review query formulation is a highly complex task done by trained information specialists. Complexity comes from the reliance on lengthy Boolean queries, which express a detailed research question. To aid query formulation, information specialists use a set of exemplar documents, called `seed studies', prior to query formulation. Seed studies help verify the effectiveness of a query prior to the full assessment of retrieved studies. Beyond this use of seeds, specific IR methods can exploit seed studies for guiding both automatic query formulation and new retrieval models. One major limitation of work to date is that these methods exploit `pseudo seed studies' through retrospective use of included studies (i.e., relevance assessments). However, we show pseudo seed studies are not representative of real seed studies used by information specialists. Hence, we provide a test collection with real world seed studies used to assist with the formulation of queries. To support our collection, we provide an analysis, previously not possible, on how seed studies impact retrieval and perform several experiments using seed-study based methods to compare the effectiveness of using seed studies versus pseudo seed studies. We make our test collection and the results of all of our experiments and analysis available at http://github.com/ielab/sysrev-seed-collection