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Found 12 papers, 8 papers with code
OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69. 5%.
Predictive Chemistry Augmented with Text Retrieval
Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from the literature.
RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing
Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature.
Multi-Vector Retrieval as Sparse Alignment
With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval.
MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation
Molecular structure recognition is the task of translating a molecular image into its graph structure.
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding
The few-shot natural language understanding (NLU) task has attracted much recent attention.
GPT Understands, Too
Prompting a pretrained language model with natural language patterns has been proved effective for natural language understanding (NLU).
GLM: General Language Model Pretraining with Autoregressive Blank Infilling
On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with 1. 25x parameters of BERT Large , demonstrating its generalizability to different downstream tasks.
Ranked #4 on
Language Modelling
on WikiText-103
(using extra training data)
GraphIE: A Graph-Based Framework for Information Extraction
Most modern Information Extraction (IE) systems are implemented as sequential taggers and only model local dependencies.
A Probabilistic Framework for Location Inference from Social Media
In this paper, we formalize the problem of inferring location from social media into a semi-supervised factor graph model (SSFGM).
Weakly Learning to Match Experts in Online Community
The challenge is how to trade off the matching degree between users' expertise and the question topic, and the likelihood of positive response from the invited users.
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction
Measuring research impact and ranking academic achievement are important and challenging problems.
