wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We propose a class of Fractional Top Trading Cycle mechanisms to solve allocation problems in which agents have fractional endowments or are ranked by coarse priorities . Dropping the graph-based definition of Top Trading Cycle , we use parameterized linear equations to describe how agents trade endowments or priorities  .
Revised:  We propose a class of Fractional Top Trading Cycle mechanisms to solve random allocation problems efficiently and fairly . Dropping the graph-based definition of Top Trading Cycle , we use parameterized linear equations to describe how agents trade endowments or priorities .
Edit intent: clarity
Original:  We propose a class of Fractional Top Trading Cycle mechanisms to solve allocation problems in which agents have fractional endowments or are ranked by coarse priorities . Dropping the graph-based definition of Top Trading Cycle , we use parameterized linear equations to describe how agents trade endowments or priorities  .
Revised:  We propose a class of Fractional Top Trading Cycle mechanisms to solve allocation problems in which agents have fractional endowments or are ranked by coarse priorities . Dropping the graph-based definition of TTC , we use parameterized linear equations to describe how agents trade endowments or priorities .
Edit intent: clarity
Original:  We propose a class of Fractional Top Trading Cycle mechanisms to solve allocation problems in which agents have fractional endowments or are ranked by coarse priorities . Dropping the graph-based definition of Top Trading Cycle , we use parameterized linear equations to describe how agents trade endowments or priorities  .
Revised:  We propose a class of Fractional Top Trading Cycle mechanisms to solve allocation problems in which agents have fractional endowments or are ranked by coarse priorities . Dropping the graph-based definition of Top Trading Cycle , we use parameterized linear equations to describe how agents trade endowments or priorities at each step .
Edit intent: clarity
Original: Efficiency and fairness are two desiderata in market design . Fairness requires randomization in many environments. As one of the few successful matching mechanisms, Top Trading Cycle (TTC) is well known for being efficient to solve deterministic allocation problems. But it is inadequate to incorporate randomization efficiently and fairly. We propose a class of Fractional TTC mechanisms to solve random allocation problems efficiently and fairly. Dropping the graph-based definition of TTC , we use parameterized linear equations to describe how agents trade endowments or priorities at each step.
Revised: Efficiency and fairness are two desiderata in market design environments. Dropping the traditional idea of clearing cycles in generated graphs , we use parameterized linear equations to describe how agents trade endowments or priorities at each step.
Edit intent: clarity
Original:  Dropping the graph-based definition of TTC , we use parameterized linear equations to describe how agents trade endowments or priorities at each step. Our mechanisms are ex-ante efficient. They satisfy various fairness axioms when parameters are properly chosen .
Revised:  Dropping the graph-based definition of TTC , we use parameterized linear equations to define trading algorithms. Our method has two advantages. First, our method avoids discussing the details of who trades with whom and how, which can be a difficult question in complex environments. Second, by controlling parameter values in our equations, our method is flexible and transparent to satisfy various fairness axioms when parameters are properly chosen .
Edit intent: clarity
Original: The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact in the context of search and recommender systems .
Revised: The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact .
Edit intent: clarity
Original: With the development of the Internet, social media has become an essential channel for posting disaster-related information . Analyzing attitudes hidden in these texts, known as sentiment analysis, is crucial for the government or relief agencies to improve disaster response efficiency, but it has not received sufficient attention  .
Revised: With the development of the Internet, social media has become an essential channel for posting disaster-related information , which provide governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient attention .
Edit intent: clarity
Original:  This paper aims to fill this gap by focusing on investigating public attitudes towards disaster response and analyzing targeted relief supplies during disaster relief. The research comprises four steps. First, this paper implements Python in grasping Twitter data, and then, we assess public perceptron quantitatively by these opinioned texts ,  which contain information like the demand for targeted relief supplies, satisfactions of disaster response and fear of the public .
Revised:  This paper aims to fill this gap by focusing on investigating public attitudes towards disaster response and analyzing targeted relief supplies during different types of disasters. We focus on different natural disasters based on properties such as types, durations, and damages, which contains a total of 41 , which contain information like the demand for targeted relief supplies, satisfactions of disaster response and fear of the public .
Edit intent: meaning-changed
Original:  First, this paper implements Python in grasping Twitter data, and then, we assess public perceptron quantitatively by these opinioned texts ,  which contain information like the demand for targeted relief supplies, satisfactions of disaster response and fear of the public .
Revised:  First, this paper implements Python in grasping Twitter data, and then, we assess public perceptron quantitatively by these opinioned texts , 993 tweets. In this paper, public perception is assessed qualitatively by manually classified tweets, which contain information like the demand for targeted relief supplies, satisfactions of disaster response and fear of the public .
Edit intent: meaning-changed
Original:  First, this paper implements Python in grasping Twitter data, and then, we assess public perceptron quantitatively by these opinioned texts ,  which contain information like the demand for targeted relief supplies, satisfactions of disaster response and fear of the public . A natural disaster dataset with sentiment labels is created, which contains 49,816 Twitter data about natural disasters in the United States. Second, this paper proposes eight machine learning models for sentiment prediction, which are the most popular models used in classification problems.
Revised:  First, this paper implements Python in grasping Twitter data, and then, we assess public perceptron quantitatively by these opinioned texts , which contain information like the demand for targeted relief supplies, satisfactions of disaster response , and public fear. Public attitudes to natural disasters are studied via a quantitative analysis using eight machine learning models for sentiment prediction, which are the most popular models used in classification problems.
Edit intent: clarity
Original:  Second, this paper proposes eight machine learning models for sentiment prediction, which are the most popular models used in classification problems. Third , the comparison of these models is conducted via various metrics, and this paper also discusses the optimization method of these models from the perspective of model parameters and input data structures.
Revised:  Second, this paper proposes eight machine learning models . To better provide decision-makers with the appropriate model , the comparison of these models is conducted via various metrics, and this paper also discusses the optimization method of these models from the perspective of model parameters and input data structures.
Edit intent: coherence
Original:  Third , the comparison of these models is conducted via various metrics, and this paper also discusses the optimization method of these models from the perspective of model parameters and input data structures. Finally, a set of real-world instances are studied from the perspective of analyzing changes of public opinion during different natural disasters and understanding the relationship between the same hazard and time series.
Revised:  Third , the comparison of machine learning models based on computational time and prediction accuracy is conducted. The change of public opinion during different natural disasters and understanding the relationship between the same hazard and time series.
Edit intent: clarity
Original:  Finally, a set of real-world instances are studied from the perspective of analyzing changes of public opinion during different natural disasters and understanding the relationship between the same hazard and time series. Results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster response .
Revised:  Finally, a set of real-world instances are studied from the perspective of analyzing changes of public opinion during different natural disasters and the evolution of people's behavior of using social media for disaster relief in the face of the identical type of natural disasters as Twitter continues to evolve are studied. The results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster response .
Edit intent: meaning-changed
Original:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%%  . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Revised:  %DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%% . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Edit intent: coherence
Original:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%%  . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Revised:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% {2}}}%DIFDELCMD < \right) %%% . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Edit intent: coherence
Original:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%%  . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Revised:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%% The implementation aspects are also described in detail with a special emphasis on the transformation of the quadratic program into a linear program that can be solved in polynomial time . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Edit intent: meaning-changed
Original:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%%  . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Revised:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%% . The algorithm was implemented in MATLAB and checked by generating five million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Edit intent: meaning-changed
Original:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%%  . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Revised:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%% . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimensions up to 30 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Edit intent: meaning-changed
Original:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%%  . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments were successful .
Revised:  A direct application of the algorithm is the 3-SAT problem, which is also guaranteed to be in the class P with a computational complexity of order O%DIFDELCMD < \left( %%% n^{\frac{45{2}}}%DIFDELCMD < \right) %%% . The algorithm was implemented in MATLAB and checked by generating one million matrices of arbitrary dimension up to 24 with random entries in the range \left[ -50,50\right] . All the experiments carried out have revealed that the method works correctly .
Edit intent: clarity
Original: In this work, we focus on large graph similarity computation problem and propose a novel "embedding-coarsening-matching" learning framework, which outperforms state-of-the-art methods in this task and has significant improvement in time efficiency. Graph similarity computation for metrics such as Graph Edit Distance (GED) is typically NP-hard , and existing heuristics-based algorithms usually achieves a unsatisfactory trade-off between accuracy and efficiency.
Revised: The ability to compute similarity scores between graphs based on metrics such as Graph Edit Distance (GED) is typically NP-hard , and existing heuristics-based algorithms usually achieves a unsatisfactory trade-off between accuracy and efficiency.
Edit intent: clarity
Original:  Graph similarity computation for metrics such as Graph Edit Distance (GED) is typically NP-hard , and existing heuristics-based algorithms usually achieves a unsatisfactory trade-off between accuracy and efficiency.
Revised:  Graph similarity computation for metrics such as Graph Edit Distance (GED) is important in many real-world applications, such as 3D action recognition and biological molecular identification. Computing exact GED values is typically an NP-hard , and existing heuristics-based algorithms usually achieves a unsatisfactory trade-off between accuracy and efficiency.
Edit intent: meaning-changed
Original:  Recently the development of deep learning techniques provides a promising solution for this problem by a data-driven approach which trains a network to encode graphs to their own feature vectors and computes similarity based on feature vectors.
Revised:  Recently , Graph Neural Networks (GNNs) provide a data-driven solution for this problem by a data-driven approach which trains a network to encode graphs to their own feature vectors and computes similarity based on feature vectors.
Edit intent: clarity
Original:  Recently the development of deep learning techniques provides a promising solution for this problem by a data-driven approach which trains a network to encode graphs to their own feature vectors and computes similarity based on feature vectors. These deep-learning methodscan be classified to two categories, embedding models and matching models. Embedding models such as GCN-Mean and GCN-Max, which directly map graphs to respective feature vectors, run faster but the performance is usually poor due to the lack of interactions across graphs. Matching models such as GMN, whose encoding process involves interaction across the two graphs, are more accurate but interaction between whole graphs brings a significant increase in time consumption (at least quadratic time complexity over number of nodes ).
Revised:  Recently the development of deep learning techniques provides a promising solution for this task, which is more efficient while maintaining prediction accuracy in small graph (around 10 nodes per graph) similarity computation. Existing GNN-based methods, which either respectively embed two graphs (lack of low-level cross-graph interactions) or deploy cross-graph interactions for whole graph pairs (redundant and time-consuming), are still not able to achieve competitive results when the number of nodes ).
Edit intent: clarity
Original:  Matching models such as GMN, whose encoding process involves interaction across the two graphs, are more accurate but interaction between whole graphs brings a significant increase in time consumption (at least quadratic time complexity over number of nodes ). Inspired by large biological molecular identification where the whole molecular is first mapped to functional groups and then identified based on these functional groups, our "embedding-coarsening-matching"
Revised:  Matching models such as GMN, whose encoding process involves interaction across the two graphs, are more accurate but interaction between whole graphs brings a significant increase in time consumption (at least quadratic time complexity over number of nodes in graphs increases. In this paper, we focus on similarity computation for large-scale graphs and propose the "embedding-coarsening-matching"
Edit intent: meaning-changed
Original:  learning framework first embeds and coarsens large graphs to coarsened graphs with denser local topology and then matching mechanism is deployed on the coarsened graphs for the final similarity scores . Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework .
Revised:  framework, which first embeds and coarsens large graphs to coarsened graphs with denser local topology and then matching mechanism is deployed on the coarsened graphs for the final similarity scores . Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework .
Edit intent: coherence
Original:  learning framework first embeds and coarsens large graphs to coarsened graphs with denser local topology and then matching mechanism is deployed on the coarsened graphs for the final similarity scores . Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework .
Revised:  learning framework first embeds and coarsens large graphs to coarsened graphs with denser local topology and then deploys fine-grained interactions on the coarsened graphs for the final similarity scores . Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework .
Edit intent: clarity
Original:  learning framework first embeds and coarsens large graphs to coarsened graphs with denser local topology and then matching mechanism is deployed on the coarsened graphs for the final similarity scores . Detailed experiments have been conducted and the results demonstrate the efficiency and effectiveness of our proposed framework .
Revised:  learning framework first embeds and coarsens large graphs to coarsened graphs with denser local topology and then matching mechanism is deployed on the coarsened graphs for the final similarity scores .
Edit intent: coherence
Original:  An external language model allows our approach to reach the quality of a comparable supervised setup and outperform a semi-supervised setup (both on test-clean). We establish a state-of-the-art result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3\% on test-clean and 13.5\% on test-other.
Revised:  An external language model allows our approach to reach the quality of a comparable supervised setup and outperform a semi-supervised setup for LibriSpeech test-clean and only 33\% worse than a comparable supervised setup. Our system establishes a competitive result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3\% on test-clean and 13.5\% on test-other.
Edit intent: meaning-changed
Original:  We establish a state-of-the-art result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3\% on test-clean and 13.5\% on test-other.
Revised:  We establish a state-of-the-art result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3\% for test-clean and 13.5\% on test-other.
Edit intent: clarity
Original:  We establish a state-of-the-art result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3\% on test-clean and 13.5\% on test-other.
Revised:  We establish a state-of-the-art result for end-to-end ASR trained on LibriSpeech train-clean-100 set with WER 4.3\% on test-clean and 13.5\% for test-other.
Edit intent: clarity
Original: Bidirectional Encoder Representations from Transformers (BERT)models for medical specialties , such as BioBERT and clinicalBERT, have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature ;
Revised: Pre-training large-scale neural language models on raw texts has made a significant contribution to improving transfer learning in natural language processing (NLP). With the introduction of transformer-based language models , such as BioBERT and clinicalBERT, have significantly improved in performing biomedical text mining tasks and have enabled extracting valuable information from biomedical literature ;
Edit intent: meaning-changed