Distributional Semantics Resources for Biomedical Text Processing

By Sampo Pyysalo, Filip Ginter, Hans Moen, Tapio Salakoski, Sophia Ananiadou. 2013

512 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e2f28568031e1902d4f8ee818261f0f2c20de6dd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language (2019, methodology): “Furthermore on MEDHOP, we equip the models with word embeddings that were pretrained on a large biomedical corpus (Pyysalo et al., 2013).”
PubMedQA: A Dataset for Biomedical Research Question Answering (2019, methodology): “BiLSTM: We simply concatenate the question and context/long answer with learnable segment embeddings appended to the biomedical word2vec embeddings (Pyysalo et al., 2013) of each token.”
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition (2018, methodology): “• C = total number of correct entities. Precision = P = C M , Recall = R = C N , F1score = 2PR P +R (15) Settings and hyperparameters We used the 200 dimensional word embedding (WE) by Pyysalo et al. [20] which was trained on PubMed, PubMed Central (PMC) and Wikipedia text, and it contains about 5 m”
How to Train good Word Embeddings for Biomedical NLP (2016, methodology): “As baselines, we include the biomedical domain vectors created by Pyysalo et al. (2013) and Kos-mopoulos et al. (2015).”
Rationale-Augmented Convolutional Neural Networks for Text Classification (2016, methodology): “6 For the RoB datasets, these were 200-dimensional and trained on biomedical texts in PubMed/PubMed Central (Pyysalo et al., 2013).”
Inferring Which Medical Treatments Work from Reports of Clinical Trials (2019, methodology): “We initialized word embeddings to pretrained word vectors induced over a large set of PubMed abstracts (Pyysalo et al., 2013).”
ERASER: A Benchmark to Evaluate Rationalized NLP Models (2019, methodology): “With the exception of the Evidence Inference dataset, these models were trained using the GLoVe (Pennington et al., 2014) 200 dimension word vectors, and Evidence Inference using the (Pyysalo et al., 2013) PubMed word vectors.”
Multichannel Convolutional Neural Network for Biological Relation Extraction (2016, methodology): “[21], while the fifth word embedding is trained by CBOW on MedLine corpus (http://www.”
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion (2015, methodology): “• General embeddings: Different from the Raw Input embeddings baseline, we use the embedding trained from a large biomedical data collection that includes knowledge bases like PubMed and PMC and a Wikipedia dump of biomedical related articles (Pyysalo et al., 2013) for semantic similarity computatio”
Deep learning for extracting protein-protein interactions from biomedical literature (2017, methodology): “In this paper, we used pre-trained word embedding vectors (Pyysalo et al., 2013) learned on PubMed articles using the word2vec tool (Mikolov et al., 2013).”
A neural joint model for entity and relation extraction from biomedical text (2017, methodology): “We used pre-trained biomedical word embeddings [33] to initial our word embeddings and other kinds of embeddings were randomly initialized in the range (-0.01, 0.01).”
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization (2017, methodology): “For the biomedical datasets, we use word2vec trained on biomedical texts (Moen and Ananiadou, 2013)11 to initialize Ep.”