wanyu/IteraTeR_full_sent huggingface dataset:
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigate whether the contrasting announcements at the highest political level might have originated disorientation among the public.
Edit intent: clarity
Original:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation among the public.
Revised:  By a sentiment analysis on tweets posted in Italian during 2018, we aimed at (i) characterising the temporal flow of communication on vaccines, (ii) evaluating the usefulness of Twitter data for estimating vaccination parameters, and (iii) investigating whether the ambiguous political communication might have originated disorientation amongst the Italian public.
Edit intent: style
Original:  Results. The population appeared to be mostly composed by serial tweeters tweeting about everything including vaccines. Tweets favourable to vaccination accounted for 75\% of retained tweets, undecided for 14\% and unfavourablefor 11\% . Twitter activity of the Italian public health institutions was negligible.
Revised:  Results. Vaccine-relevant tweeters interactions peaked in response to main political events. Out of retained tweets, undecided for 14\% and unfavourablefor 11\% . Twitter activity of the Italian public health institutions was negligible.
Edit intent: meaning-changed
Original:  Tweets favourable to vaccination accounted for 75\% of retained tweets, undecided for 14\% and unfavourablefor 11\% . Twitter activity of the Italian public health institutions was negligible. After smoothing the temporal pattern, an up-and-down trend in the favourable proportion emerged, synchronized with the switch between governments, providing clear evidence of disorientation  . Conclusion.
Revised:  Tweets favourable to vaccination accounted for 75\% of retained tweets, 70.0\% resulted favourable to vaccination, 16.5\% unfavourable, and 13.6\% undecided, respectively. The smoothed time series of polarity proportions exhibit frequent large changes in the favourable proportion emerged, synchronized with the switch between governments, providing clear evidence of disorientation . Conclusion.
Edit intent: meaning-changed
Original:  This is especially true given the increasing role of online social media as information source, which might yield to social pressures eventually harmful for vaccine uptake, and is worsened by the lack of institutional presence on Twitter . This calls for efforts to contrast misinformation and the ensuing spread of hesitancy.
Revised:  This is especially true given the increasing role of online social media as information source, which might yield to social pressures eventually harmful for vaccine uptake, and is worsened by the lack of institutional presence on Twitter , calling for efforts to contrast misinformation and the ensuing spread of hesitancy.
Edit intent: fluency
Original: We develop a target zone model with realistic features such as finite exit time , non-stationary dynamics and heavy tails. Our rigorous characterization of risk corresponds to the dynamic counterpart of a mean-preserving spread.
Revised: We develop a target zone model with realistic features such as finite exit time and non-Gaussian tails. We show how the tails are a consequence of time-varying investor risk aversion, which generates mean-preserving spread.
Edit intent: meaning-changed
Original:  Our rigorous characterization of risk corresponds to the dynamic counterpart of a mean-preserving spread. We explicitly solve for both stationary and transient exchange rate paths, and show how they are influenced by the distance to both the time horizon and the target zone bands.
Revised:  Our rigorous characterization of risk corresponds to the dynamic counterpart of a mean-preserving spreads in the fundamental distribution. We solve explicitly for stationary and non-stationary exchange rate paths, and show how they are influenced by the distance to both the time horizon and the target zone bands.
Edit intent: meaning-changed
Original:  We prove that the interplay of the diffusive component and the destabilizing risk component can yield an endogenous regime shift characterized by a threshold level of risk above which the target zone ceases to exist. All the previous results cannot obtain by means of the standard Gaussian and affine models.
Revised:  We prove that increases in risk after a certain threshold can yield endogenous regime shifts where the "honeymoon effects" vanish and the target zone cannot be feasibly maintained. None of these results can be obtained by means of the standard Gaussian and affine models.
Edit intent: clarity
Original:  One obstacle in this large dimensional setup is the singularity of the sample covariance matrix of the excess asset returns. To solve this issue, we benefit from a technique called nodewise regression , which was developed by Meinshausen and Buhlmann (2006). It provides a sparse/weakly sparse and consistent estimate of the precision matrix , using the Lasso method.
Revised:  One obstacle in this large dimensional setup is the singularity of the sample covariance matrix of the idiosyncratic terms is assumed to be sparse, but the respective covariance matrix can be non-sparse. Since the nodewise regression is not feasible due to the unknown nature of errors, we provide a feasible-residual-based nodewise regression to estimate the precision matrix , using the Lasso method.
Edit intent: meaning-changed
Original:  It provides a sparse/weakly sparse and consistent estimate of the precision matrix , using the Lasso method. We analyze three issues. One of the key results in our paper is that mean-variance efficiency for the portfolios in large dimensions is established. Then tied to that result , we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets.
Revised:  It provides a sparse/weakly sparse and consistent estimate of the precision matrix of errors as a new method. Next, we show that the residual-based nodewise regression provides a consistent estimate for the precision matrix of errors. In another new development , we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets.
Edit intent: meaning-changed
Original:  Then tied to that result , we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets.
Revised:  Then tied to that result , we also show that the precision matrix of returns can be estimated consistently, even with an increasing number of factors. Benefiting from the consistency of the precision matrix estimate of returns, we show that: (1) the portfolios in high dimensions are mean-variance efficient; (2) maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets.
Edit intent: meaning-changed
Original:  Then tied to that result , we also show that the maximum out-of-sample Sharpe ratio can be consistently estimated in this large portfolio of assets. Furthermore, we provide convergence rates and see thatthe number of assets slow down the convergence up to a logarithmic factor .
Revised:  Then tied to that result , we also show that the maximum out-of-sample Sharpe ratio estimator is consistent and the number of assets slow down the convergence up to a logarithmic factor .
Edit intent: clarity
Original:  Furthermore, we provide convergence rates and see thatthe number of assets slow down the convergence up to a logarithmic factor . Then, we provide consistency of maximum Sharpe Ratio when the portfolio weights add up to one, and also provide a new formula and an estimate for constrained maximum Sharpe ratio .
Revised:  Furthermore, we provide convergence rates and see thatthe number of assets slow down the convergence up to a logarithmic factor ; (3) the maximum Sharpe ratio estimator is consistent when the portfolio weights add up to one, and also provide a new formula and an estimate for constrained maximum Sharpe ratio .
Edit intent: meaning-changed
Original:  Then, we provide consistency of maximum Sharpe Ratio when the portfolio weights add up to one, and also provide a new formula and an estimate for constrained maximum Sharpe ratio . Finally, we provide consistent estimates of the Sharpe ratios of global minimum variance portfolio and Markowitz's (1952) mean variance portfolio. In terms of assumptions, we allow for time series data. Simulation and out-of-sample forecasting exercise shows that our new method performs well compared to factor and shrinkage based techniques .
Revised:  Then, we provide consistency of maximum Sharpe Ratio when the portfolio weights sum to one; and (4) the Sharpe ratio estimators are consistent in global minimum-variance and mean-variance portfolios .
Edit intent: coherence
Original:  Results on three knowledge-driven tasks (total six datasets) including relation classification, entity typing  and question answering  demonstrate that each adapter improves the performance , and the combination of both adapters brings further improvements.
Revised:  Results on three knowledge-driven tasks (total six datasets) including relation classification, entity typing , and question answering demonstrate that each adapter improves the performance , and the combination of both adapters brings further improvements.
Edit intent: fluency
Original:  Results on three knowledge-driven tasks (total six datasets) including relation classification, entity typing  and question answering  demonstrate that each adapter improves the performance , and the combination of both adapters brings further improvements.
Revised:  Results on three knowledge-driven tasks (total six datasets) including relation classification, entity typing and question answering , demonstrate that each adapter improves the performance , and the combination of both adapters brings further improvements.
Edit intent: fluency
Original: We introduce the problem of Automatic Location Type Classificationfrom social media posts.
Revised: Associating type to locations can be used to enrich maps and can serve a plethora of geospatial applications. An automatic method to do so could make the process less expensive in terms of human labor, and faster to react to changes. In this paper we study the problem of Geosocial Location Classification, where the type of a site, e.g., a building, is discovered based on social-media posts.
Edit intent: coherence
Original:  We provide a dataset of locations associated with tweets posted in close geographical proximity. We explore two approaches to the problem: (a) a pipeline approach  where each message is first classified, and then the location associated with the message set is inferred from the individual message labels;
Revised:  We explore two approaches to the problem: (a) a pipeline approach where each message is first classified, and then the location associated with the message set is inferred from the individual message labels;
Edit intent: coherence
Original:  Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, simpler linear classifiers outperform deep neural network alternatives that have shown superior in previous text classification tasks .
Revised:  Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, linear classifiers outperform deep neural network alternatives that have shown superior in previous text classification tasks .
Edit intent: clarity
Original:  Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, simpler linear classifiers outperform deep neural network alternatives that have shown superior in previous text classification tasks .
Revised:  Moreover, we show that due to the unique structure of the problem, where weakly-related messages are jointly processed to yield a single final label, simpler linear classifiers outperform deep neural network alternatives .
Edit intent: clarity
Original: This paper presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets  . Polylidar is able to extract multiple disjoint polygons and capture interior holes.
Revised: This paper presents Polylidar, an efficient algorithm to extract non-convex polygons from 2D point sets , including interior holes. Plane segmented point clouds can be input into Polylidar to extract their polygonal counterpart, thereby reducing map size and improving visualization . Polylidar is able to extract multiple disjoint polygons and capture interior holes.
Edit intent: meaning-changed
Original:  The key to Polylidar's speed, and the main contribution of this paper, is in efficiently transforming each triangular mesh into a concave polygon . This paper describes Polylidar and provides benchmarks to comparatively evaluate its speed and accuracy.
Revised:  Finally each region is converted to a polygon through a novel boundary following method which accounts for holes. Real-world and synthetic benchmarks are presented to comparatively evaluate its speed and accuracy.
Edit intent: meaning-changed
Original:  Modeling the two sources of stochasticity separately allows researchers to differentiate between biological stochasticity (e.g., in birth processes) and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled.
Revised:  Modeling the two sources of stochasticity separately allows researchers to differentiate between biological variation (e.g., in birth processes) and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled.
Edit intent: clarity
Original:  However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. In addition, many SSM users are unaware of the potential estimation problemsthey could encounter, and of the model selection and validation tools that can help them assess how well their models fit their data.
Revised:  However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. We provide the knowledge for ecologists to create SSMs that are robust to common, and often hidden, estimation problems, and the model selection and validation tools that can help them assess how well their models fit their data.
Edit intent: coherence
Original: Crude oil price forecasting has attracted substantial attention in the field of forecasting. Recently, the research on text-based crude oil price forecasting has advanced.
Revised: Sparse and short news headlines can be arbitrary, noisy, and ambiguous, making it difficult for classic topic model LDA designed for accommodating long text to discover knowledge from them. Nonetheless, some of the existing research about text-based crude oil price forecasting has advanced.
Edit intent: meaning-changed
Original:  Recently, the research on text-based crude oil price forecasting has advanced. To improve accuracy, some studies have added as many covariates as possible, such as textual and nontextual factors, to their models, leading to unnecessary human intervention and computational costs. Moreover, some methods are only designed for crude oil forecasting and cannot be well transferred to the forecasting of other similar futures commodities. In contrast, this article proposes a text-based forecasting framework for futures commodities that uses only future news headlinesobtained from Investing.com to forecast crude oil prices. Two marketing indexes, the sentiment index and the topic intensity index, are extracted from these news headlines . Considering that the public's sentiment changes over time, the time factor is innovatively applied to the construction of the sentiment index.
Revised:  Recently, the research on text-based crude oil forecasting employs LDA to explore topics from news headlines, resulting in a mismatch between the short text and the topic intensity index, are extracted from these news headlines . Considering that the public's sentiment changes over time, the time factor is innovatively applied to the construction of the sentiment index.
Edit intent: meaning-changed
Original:  Two marketing indexes, the sentiment index and the topic intensity index, are extracted from these news headlines . Considering that the public's sentiment changes over time, the time factor is innovatively applied to the construction of the sentiment index. Taking the nature of the short news headlines into consideration, a short text topic model called SeaNMF is used to calculate the topic intensity of the futures market more accurately. Two methods, VAR and RFE, are used for lag order judgment and feature selection, respectively, at the model construction stage. The experimental results show that the Ada-text model outperforms the Adaboost.RT baseline model and the other benchmarks .
Revised:  Two marketing indexes, the sentiment index and the topic model and further affecting the forecasting performance. Exploiting advanced and appropriate methods to construct high-quality features from news headlines becomes crucial in crude oil forecasting. To tackle this issue, this paper introduces two novel indicators of topic and sentiment for the short and sparse text data. Empirical experiments show that AdaBoost.RT with our proposed text indicators, with a more comprehensive view and characterization of the short and sparse text data, outperforms the other benchmarks. Another significant merit is that our method also yields good forecasting performance when applied to other futures commodities .
Edit intent: coherence
Original:  We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for a CRRA (i.e., constant relative risk aversion ) utility function .
Revised:  We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion ) utility function .
Edit intent: clarity
Original:  We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for a CRRA (i.e., constant relative risk aversion ) utility function .
Revised:  We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for a CRRA (i.e., constant relative risk aversion (CRRA) utility functions .
Edit intent: clarity
Original:  In our non-concave optimization problem, we find that  for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, assuming that the regulation only protects the debt holders' benefit to a certain level .
Revised:  In our non-concave optimization problem, we find that with a not too strict regulation for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, assuming that the regulation only protects the debt holders' benefit to a certain level .
Edit intent: meaning-changed
Original:  In our non-concave optimization problem, we find that  for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, assuming that the regulation only protects the debt holders' benefit to a certain level .
Revised:  In our non-concave optimization problem, we find that for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, which shows on some level the ineffectiveness of the ES-based regulation .
Edit intent: meaning-changed
Original: This paper studies a non-concave optimization problem under a Value-at-Risk (VaR) or an Expected Shortfall (ES) constraint . The non-concavity of the problem stems from the non-linear payoff structure of the optimizing investor .
Revised: This paper studies a non-concave optimization problem under a Value-at-Risk (VaR) or an Expected Shortfall (ES) constraint corresponding to a non-concave optimization problem under constraints .
Edit intent: clarity
Original:  We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion (CRRA) utility functions.
Revised:  We obtain the closed-form optimal wealth with the ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion (CRRA) utility functions.
Edit intent: fluency
Original:  We obtain the closed-form optimal wealth with an ES constraint as well as with a VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion (CRRA) utility functions.
Revised:  We obtain the closed-form optimal wealth with an ES constraint as well as with the VaR constraint respectively, and explicitly calculate the optimal trading strategy for constant relative risk aversion (CRRA) utility functions.
Edit intent: fluency
Original:  In our non-concave optimization problem, we find that with a not too strict regulation for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, which shows on some level the ineffectiveness of the ES-based  regulation  .
Revised:  We find that both VaR and ES-based regulation can effectively reduce the probability of default for a surplus-driven financial institution. However, the liability holders' benefits cannot be fully protected under either VaR- or ES-based regulation .
Edit intent: coherence
Original:  In our non-concave optimization problem, we find that with a not too strict regulation for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, which shows on some level the ineffectiveness of the ES-based  regulation  .
Revised:  In our non-concave optimization problem, we find that with a not too strict regulation for any VaR-constraint with an arbitrary risk level, there exists an ES-constraint leading to the same investment strategy, which shows on some level the ineffectiveness of the ES-based regulation. In addition, we show that the VaR and ES-based regulation .
Edit intent: meaning-changed