Gamal Crichton

University of Cambridge | Cam · Department of Theoretical and Applied Linguistics

Literature-based Discovery (LBD) aims to discover new knowledge automatically from large collections of literature. Scientific literature is growing at an exponential rate, making it difficult for researchers to stay current in their discipline and easy to miss knowledge necessary to advance their research. LBD can facilitate hypothesis testing and...

Literature-based Discovery (LBD) uses information from explicit statements in literature to generate new or unstated knowledge. Automated LBD can thus facilitate hypothesis testing and generation from large collections of publications to support and accelerate scientific research, which is adversely affected by publication explosion and knowledge f...

Background: Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neu...

Background: Link prediction in biomedical graphs has several important applications including predicting Drug-Target Interactions (DTI), Protein-Protein Interaction (PPI) prediction and Literature-Based Discovery (LBD). It can be done using a classifier to output the probability of link formation between nodes. Recently several works have used neur...

Background: Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them toge...

\textbf{Background}$ Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use t...

Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture for combining alternative word representations. By using an attention mechanism, the model is able to dynamically decide how...

Exploratory Search (ES) is a technique used when searching to learn or when an information need is poorly defined and cannot be easily formed into a query. Work done in the field has focused on providing interfaces and on systems which can either handle restricted, domain-specific collections or are focused on providing an exploratory feature for u...