Papers by Petr Knoth
Dynamic Context Extraction for Citation Classification (2022.aacl-main)
Copied to clipboard
| Challenge: | Prior studies have focused on the application of fixed-size contiguous citation contexts or manually curated citation contextual contexts. |
| Approach: | They propose an automated unsupervised approach for the selection of a dynamic-size and potentially non-contiguous citation context based on transformer-based document representations and embedding similarities. |
| Outcome: | The proposed model improves on the domain-specific and multi-disciplinary datasets, irrespective of the dataset's domain. |
Deduplication of Scholarly Documents using Locality Sensitive Hashing and Word Embeddings (2020.lrec-1)
Copied to clipboard
| Challenge: | Deduplication is the task of identifying near and exact duplicate data items in a collection. |
| Approach: | They propose a method which uses locality sensitive hashing and meaning representation to determine (near) duplicates. |
| Outcome: | The proposed method achieves a macro F1-score of 0.90 on a ground truth dataset of 100K scholarly documents. |
ACT2: A multi-disciplinary semi-structured dataset for importance and purpose classification of citations (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for classifying citations rely on bibliometric measures to consider the semantics of citation. |
| Approach: | They propose to use a Citation Context Classification (3C) shared task dataset to classify citations according to their purpose and importance. |
| Outcome: | The proposed model can be used to link research works to graphs and enable efficient knowledge discovery. |
Analyzing Citation-Distance Networks for Evaluating Publication Impact (L18-1)
Copied to clipboard
| Challenge: | citation networks are used to study scholarly articles' semantic distances and their referencing patterns. |
| Approach: | They propose to analyze the semantic distance of scholarly articles in a citation network to uncover patterns that reflect scientific impact. |
| Outcome: | The proposed method combines semantic distance and content similarity to uncover scientific impact of articles in two different types of publications. |