Papers by Mark Stevenson
Combining Counting Processes and Classification Improves a Stopping Rule for Technology Assisted Review (2023.findings-emnlp)
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| Challenge: | Experiments on multiple data sets show that the proposed approach consistently improves performance and outperforms several alternative methods. |
| Approach: | They propose to integrate a text classifier into an existing TAR stopping rule to train it without the need for additional annotations. |
| Outcome: | Experiments on multiple data sets show the proposed approach outperforms other methods and achieves the desired level of recall with a lower cost than the existing method based on counting processes alone. |
ParaPat: The Multi-Million Sentences Parallel Corpus of Patents Abstracts (2020.lrec-1)
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| Challenge: | Existing parallel corpora for patents and scientific texts are not available due to the need for correct alignment and human curation. |
| Approach: | They develop a parallel corpus from the open access Google Patents dataset . they use Hunalign algorithm to align sentences and tokens using the largest 22 languages . |
| Outcome: | The proposed corpus is available in TSV format and with a SQLite database, with complementary information regarding patent metadata. |
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis (2021.naacl-main)
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| Challenge: | Knowledge Graph Embeddings (KGEs) have been explored in recent years due to their promise for a wide range of applications. |
| Approach: | They propose a KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches. |
| Outcome: | The proposed framework reduces the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches while producing competitive performance. |
HiDE: a Tool for Unrestricted Literature Based Discovery (C18-2)
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| Challenge: | Literature based discovery (LBD) is an automatic technique that infers as yet unobserved connections from literature. |
| Approach: | They propose a literature based discovery tool which allows fast access to hidden connections generated from all abstracts in Medline. |
| Outcome: | The tool allows users to explore the full range of hidden connections generated by an LBD system. |
Modelling Stopping Criteria for Search Results using Poisson Processes (D19-1)
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| Challenge: | Document retrieval systems often return large sets of documents, especially when applied to large collections. |
| Approach: | They propose a method that predicts the rate at which relevant documents occur using a Poisson process and allows a user to specify a minimum desired level of recall to achieve. |
| Outcome: | The proposed method is compared with previous methods on a public dataset and compares it with existing methods. |
Topic or Style? Exploring the Most Useful Features for Authorship Attribution (C18-1)
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| Challenge: | Existing approaches to authorship attribution rely on individual's writing style and/or preferred topics. |
| Approach: | They analyse four widely used datasets to explore how different types of features affect authorship attribution accuracy under varying conditions. |
| Outcome: | The proposed model outperforms the state-of-the-art on two out of the four datasets used. |
Document Set Expansion with Positive-Unlabeled Learning Using Intractable Density Estimation (2024.lrec-main)
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| Challenge: | Existing approaches to Document Set Expansion (DSE) rely on the unrealistic assumption of knowing the class prior for positive samples in the collection. |
| Approach: | They propose a novel method that utilizes intractable density estimation models to learn the class prior for positive samples in the collection. |
| Outcome: | The proposed method is based on a set of examples from PubMed and Covid datasets in a transductive setting. |
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)
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| Challenge: | Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias. |
| Approach: | They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies. |
| Outcome: | The proposed methods are not robust and cannot identify common types of bias, while analogies are unsuitable indicators. |
Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation (2021.naacl-main)
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| Challenge: | Existing methods for building high-quality CLWEs learn mappings that minimise the l2 norm loss function but this optimisation objective has been shown to be sensitive to outliers. |
| Approach: | They propose a simple post-processing step to improve cross-lingual word embeddings using the Manhattan norm goodness-of-fit criterion. |
| Outcome: | The proposed approach outperforms four state-of-the-art baselines in bilingual lexicon induction and cross-lingual transfer tasks. |