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Essay Checker: Free Online Paper Corrector

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Why Should You Use a Free Essay Checker?

The simple answer? Good grammar is necessary, but it’s not easy. You've already done countless hours of research to write the essay. You don’t want to spend countless hours correcting it, too.

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Essay writing can be a long and tedious process. ProWritingAid's essay checker saves you the hassle by acting as the first line of defense against pesky grammar issues.

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Good Writing = Good Grades

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How Does ProWritingAid’s Essay Checker Work?

Your goal in essay writing is to convey your message as best as possible. ProWritingAid's essay checker is the first step towards doing this.

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ProWritingAid’s essay checker will show you what it thinks are spelling errors and present you with possible corrections. If a word is flagged and it’s actually spelt correctly you can always choose to ignore the suggestion.

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Professors aren’t fans of poor grammar because it interrupts your message and makes your essay hard to understand. ProWritingAid will run a grammar check on your paper to ensure that your message is precise and is being communicated the way you intended.

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A missing period or comma here and there may not seem that serious, but you’ll lose marks for punctuation errors. Run ProWritingAid’s paper checker to use the correct punctuation marks every time and elevate your writing.

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What Else Can the Essay Checker Do?

The editing tool analyzes your text and highlights a variety of key writing issues, such as overused words, incohesive sentence structures, punctuation issues, repeated phrases, and inconsistencies.

You don’t need to drown your essay in words just to meet the word count. ProWritingAid’s essay checker will help to make your words more effective. You'll get to construct your arguments and make sure that every word you use builds towards a meaningful conclusion.

Transition words help to organize your ideas by showing the relationship between them. The essay checker has a built in Transition report that highlights and shows the percentage of transitions used in your essay. Use the results to add transitions where necessary.

An engaging essay has sentences of varying lengths. Don’t bore your professor with long, rambling sentences. The essay checker will show you where you need to break long sentences into shorter sentences, or add more sentence length variation.

Generally, in scholarly writing, with its emphasis on precision and clarity, the active voice is preferred. However, the passive voice is acceptable in some instances. When you run your essay through ProWritingAid’s essay checker, you get feedback on whether you 'r e using the passive or active voice to convey your idea.

There are academic specific power verbs like appraise , investigate , debunk , support , etc., that can add more impact to your argument by giving a more positive and confident tone. The essay checker will check your writing for power verbs and notify you if you have less than three throughout your essay.

It's easy to get attached to certain phrases and use them as crutches in your essays but this gives the impression of boring and repetitive writing. The essay checker will highlight your repeats and suggest contextually relevant alternatives.

Gain access to in-house blog reports on citations, how to write a thesis statement, how to write a conclusion, and more. Venture into a world of resources specific to your academic needs.

What Kinds of Papers Does ProWritingAid Correct?

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Questions & Answers

1. how do i use the essay checker online tool.

You can either copy and paste your essay in the essay checker field or upload your essay from your computer. Your suggestions will show once you enter text. You’ll see a number of possible grammar and spelling issues. Sign up for free to get unlimited suggestions to improve your writing style, grammar, and sentence structure. Avoid unintentional plagiarism with a premium account.

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The essay checker works with both British English and American English. Just choose the one you would like to use and your corrections will reflect this.

3. Is using an essay checker cheating?

No. The essay checker won’t ever write the essay for you. It will point out possible edits and advise you on changes you need to make. You have full autonomy and get to decide which changes to accept.

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The essay writing power remains in your hands. You choose which suggestions you want to accept and you can ignore those that you don’t think apply.

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Students who have an eligible student email address can get 20% off ProWritingAid Premium. Email [email protected] from your student email address to access your discount.

6. Does ProWritingAid have a plagiarism checker?

Yes! ProWritingAid’s plagiarism checker will check your work against over a billion web-pages, published works, and academic papers, so you can be sure of its originality. Find out more about pricing for plagiarism checks here .

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Tell people you read and write for a living, and they picture a ghostly creature, an idea only incidentally appended to a body. What they often fail to understand is that the life of the mind is also a physical life — a life spent lugging irksomely heavy volumes around on the Metro and annotating their margins with a cramping hand. The poet, essayist and New York Times poetry columnist Elisa Gabbert is rare in grasping that reading is, in addition to a mental exercise, a movement performed in a particular place.

“If I remember anything about a book, I also remember where I read it — what room, what chair,” she writes in her charming new essay collection, “ Any Person Is the Only Self .” Writing, too, proves spatial: “I think essays, like buildings, need structure and mood. The first paragraph should function as a foyer or an antechamber, bringing you into the mood.”

The 16 delightfully digressive pieces in this collection are all moods that involve books in one way or another. But they are not just about the content of books, although they are about that, too: They are primarily about the acts of reading and writing, which are as much social and corporeal as cerebral.

In the first essay — the foyer — Gabbert writes about the shelf of newly returned books at her local library. “The books on that shelf weren’t being marketed to me,” she writes. “They weren’t omnipresent in my social media feeds. They were very often old and very often ugly. I came to think of that shelf as an escape from hype.” The haphazard selections on the shelf were also evidence of other people — the sort of invisible but palpable community of readers that she came to miss so sharply during the pandemic.

In another essay, she learns of a previously unpublished story by one of her favorite authors, Sylvia Plath, who makes frequent appearances throughout this book. Fearing that the story will disappoint her, Gabbert puts off reading it. As she waits, she grows “apprehensive, even frightened.”

There are writers who attempt to excise themselves from their writing, to foster an illusion of objectivity; thankfully, Gabbert is not one of them. On the contrary, her writing is full of intimacies, and her book is a work of embodied and experiential criticism, a record of its author’s shifting relationships with the literature that defines her life. In one piece, she rereads and reappraises books she first read as a teenager; in another, she and her friends form a “Stupid Classics Book Club,” to tackle “all the corny stuff from the canon that we really should have read in school but never had.”

Gabbert is a master of mood, not polemic, and accordingly, her writing is not didactic; her essays revolve around images and recollections rather than arguments. In place of the analytic pleasures of a robustly defended thesis, we find the fresh thrills of a poet’s perfected phrases and startling observations. “Parties are about the collective gaze, the ability to be seen from all angles, panoramically,” she writes in an essay about fictional depictions of parties. She describes the photos in a book by Rachael Ray documenting home-cooked meals — one of the volumes on the recently returned shelf — as “poignantly mediocre.” Remarking on a listicle of “Books to Read by Living Women (Instead of These 10 by Dead Men),” Gabbert wonders, “Since when is it poor form to die?”

“Any Person Is the Only Self” is both funny and serious, a winning melee of high and low cultural references, as packed with unexpected treasures as a crowded antique shop. An academic text on architecture, the Austrian poet Rainer Maria Rilke, a rare memory disorder whose victims recall every aspect of their autobiographies in excruciatingly minute detail, “Madame Bovary,” YouTube videos about people who work as professional cuddlers, a psychological study about whether it is possible to be sane in an insane asylum — all these feature in Gabbert’s exuberant essays. She is a fiercely democratic thinker, incapable of snobbery and brimming with curiosity.

Perhaps because she is so indefatigably interested, she gravitates toward writers who see literature as a means of doubling life, allowing it to hold twice as much. Plath confessed in her journals that she wrote in an attempt to extend her biography beyond its biological terminus: “My life, I feel, will not be lived until there are books and stories which relive it perpetually in time.” The very act of keeping a diary, then, splits the self in two.

Plath once insisted that bad things could never happen to her and her peers because “we’re different.” Gabbert asks “Different why?” and concludes that everyone is different: “We are we , not them. Any person is the only self.” But that “only” is, perhaps counterintuitively, not constrained or constricted. Walt Whitman famously wrote that his only self comprised “multitudes,” and Gabbert echoes him when she reflects, “If there is no one self, you can never be yourself, only one of your selves.” And indeed, she is loath to elevate any of her many selves over any of the others. When she rereads a book that she loved in her adolescence, she thinks she was right to love it back then. “That self only knew what she knew,” she writes. “That self wasn’t wrong .” Both her past self and her present self have an equal claim to being Elisa Gabbert, who is too fascinated by the world’s manifold riches to confine herself to a single, limited life.

Becca Rothfeld is the nonfiction book critic for The Washington Post and the author of “All Things Are Too Small: Essays in Praise of Excess.”

Any Person Is the Only Self

By Elisa Gabbert

FSG Originals. 230 pp. $18, paperback.

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NeurIPS 2024 Datasets and Benchmarks Track

If you'd like to become a reviewer for the track, or recommend someone, please use this form .

The Datasets and Benchmarks track serves as a venue for high-quality publications, talks, and posters on highly valuable machine learning datasets and benchmarks, as well as a forum for discussions on how to improve dataset development. Datasets and benchmarks are crucial for the development of machine learning methods, but also require their own publishing and reviewing guidelines. For instance, datasets can often not be reviewed in a double-blind fashion, and hence full anonymization will not be required. On the other hand, they do require additional specific checks, such as a proper description of how the data was collected, whether they show intrinsic bias, and whether they will remain accessible. The Datasets and Benchmarks track is proud to support the open source movement by encouraging submissions of open-source libraries and tools that enable or accelerate ML research.

The previous editions of the Datasets and Benchmarks track were highly successful; you can view the accepted papers from 2021 , 2002 , and 2023 , and the winners of the best paper awards 2021 , 2022 and 2023

CRITERIA. W e are aiming for an equally stringent review as the main conference, yet better suited to datasets and benchmarks. Submissions to this track will be reviewed according to a set of criteria and best practices specifically designed for datasets and benchmarks , as described below. A key criterion is accessibility: datasets should be available and accessible , i.e. the data can be found and obtained without a personal request to the PI, and any required code should be open source. We encourage the authors to use Croissant format ( https://mlcommons.org/working-groups/data/croissant/ ) to document their datasets in machine readable way.   Next to a scientific paper, authors should also submit supplementary materials such as detail on how the data was collected and organised, what kind of information it contains, how it should be used ethically and responsibly, as well as how it will be made available and maintained.

RELATIONSHIP TO NeurIPS.  Submissions to the track will be part of the main NeurIPS conference , presented alongside the main conference papers. Accepted papers will be officially published in the NeurIPS proceedings .

SUBMISSIONS.  There will be one deadline this year. It is also still possible to submit datasets and benchmarks to the main conference (under the usual review process), but dual submission to both is not allowed (unless you retracted your paper from the main conference). We also cannot transfer papers from the main track to the D&B track. Authors can choose to submit either single-blind or double-blind . If it is possible to properly review the submission double-blind, i.e., reviewers do not need access to non-anonymous repositories to review the work, then authors can also choose to submit the work anonymously. Papers will not be publicly visible during the review process. Only accepted papers will become visible afterward. The reviews themselves are not visible during the review phase but will be published after decisions have been made. The datasets themselves should be accessible to reviewers but can be publicly released at a later date (see below). New authors cannot be added after the abstract deadline and they should have an OpenReview profile by the paper deadline. NeurIPS does not tolerate any collusion whereby authors secretly cooperate with reviewers, ACs or SACs to obtain favourable reviews.

SCOPE. This track welcomes all work on data-centric machine learning research (DMLR) and open-source libraries and tools that enable or accelerate ML research, covering ML datasets and benchmarks as well as algorithms, tools, methods, and analyses for working with ML data. This includes but is not limited to:

• New datasets, or carefully and thoughtfully designed (collections of) datasets based on previously available data.
• Data generators and reinforcement learning environments.
• Data-centric AI methods and tools, e.g. to measure and improve data quality or utility, or studies in data-centric AI that bring important new insight.
• Advanced practices in data collection and curation that are of general interest even if the data itself cannot be shared.
• Frameworks for responsible dataset development, audits of existing datasets, identifying significant problems with existing datasets and their use
• Benchmarks on new or existing datasets, as well as benchmarking tools.
• In-depth analyses of machine learning challenges and competitions (by organisers and/or participants) that yield important new insight.
• Systematic analyses of existing systems on novel datasets yielding important new insight.

Read our original blog post for more about why we started this track.

Important dates

• Abstract submission deadline: May 29, 2024
• Full paper submission and co-author registration deadline: Jun 5, 2024
• Supplementary materials submission deadline: Jun 12, 2024
• Review deadline - Jul 24, 2024
• Release of reviews and start of Author discussions on OpenReview: Aug 07, 2024
• End of author/reviewer discussions on OpenReview: Aug 31, 2024
• Author notification: Sep 26, 2024
• Camera-ready deadline: Oct 30, 2024 AOE

Note: The site will start accepting submissions on April 1 5 , 2024.

FREQUENTLY ASKED QUESTIONS

Q: My work is in scope for this track but possibly also for the main conference. Where should I submit it?

A: This is ultimately your choice. Consider the main contribution of the submission and how it should be reviewed. If the main contribution is a new dataset, benchmark, or other work that falls into the scope of the track (see above), then it is ideally reviewed accordingly. As discussed in our blog post, the reviewing procedures of the main conference are focused on algorithmic advances, analysis, and applications, while the reviewing in this track is equally stringent but designed to properly assess datasets and benchmarks. Other, more practical considerations are that this track allows single-blind reviewing (since anonymization is often impossible for hosted datasets) and intended audience, i.e., make your work more visible for people looking for datasets and benchmarks.

Q: How will paper accepted to this track be cited?

A: Accepted papers will appear as part of the official NeurIPS proceedings.

Q: Do I need to submit an abstract beforehand?

A: Yes, please check the important dates section for more information.

Q: My dataset requires open credentialized access. Can I submit to this track?

A: This will be possible on the condition that a credentialization is necessary for the public good (e.g. because of ethically sensitive medical data), and that an established credentialization procedure is in place that is 1) open to a large section of the public, 2) provides rapid response and access to the data, and 3) is guaranteed to be maintained for many years. A good example here is PhysioNet Credentialing, where users must first understand how to handle data with human subjects, yet is open to anyone who has learned and agrees with the rules. This should be seen as an exceptional measure, and NOT as a way to limit access to data for other reasons (e.g. to shield data behind a Data Transfer Agreement). Misuse would be grounds for desk rejection. During submission, you can indicate that your dataset involves open credentialized access, in which case the necessity, openness, and efficiency of the credentialization process itself will also be checked.

SUBMISSION INSTRUCTIONS

A submission consists of:

• Please carefully follow the Latex template for this track when preparing proposals. We follow the NeurIPS format, but with the appropriate headings, and without hiding the names of the authors. Download the template as a bundle here .
• Papers should be submitted via OpenReview
• Reviewing is in principle single-blind, hence the paper should not be anonymized. In cases where the work can be reviewed equally well anonymously, anonymous submission is also allowed.
• During submission, you can add a public link to the dataset or benchmark data. If the dataset can only be released later, you must include instructions for reviewers on how to access the dataset. This can only be done after the first submission by sending an official note to the reviewers in OpenReview. We highly recommend making the dataset publicly available immediately or before the start of the NeurIPS conference. In select cases, requiring solid motivation, the release date can be stretched up to a year after the submission deadline.
• Dataset documentation and intended uses. Recommended documentation frameworks include datasheets for datasets , dataset nutrition labels , data statements for NLP , data cards , and accountability frameworks .
• URL to website/platform where the dataset/benchmark can be viewed and downloaded by the reviewers. 
• URL to Croissant metadata record documenting the dataset/benchmark available for viewing and downloading by the reviewers. You can create your Croissant metadata using e.g. the Python library available here: https://github.com/mlcommons/croissant
• Author statement that they bear all responsibility in case of violation of rights, etc., and confirmation of the data license.
• Hosting, licensing, and maintenance plan. The choice of hosting platform is yours, as long as you ensure access to the data (possibly through a curated interface) and will provide the necessary maintenance.
• Links to access the dataset and its metadata. This can be hidden upon submission if the dataset is not yet publicly available but must be added in the camera-ready version. In select cases, e.g when the data can only be released at a later date, this can be added afterward (up to a year after the submission deadline). Simulation environments should link to open source code repositories
• The dataset itself should ideally use an open and widely used data format. Provide a detailed explanation on how the dataset can be read. For simulation environments, use existing frameworks or explain how they can be used.
• Long-term preservation: It must be clear that the dataset will be available for a long time, either by uploading to a data repository or by explaining how the authors themselves will ensure this
• Explicit license: Authors must choose a license, ideally a CC license for datasets, or an open source license for code (e.g. RL environments). An overview of licenses can be found here: https://paperswithcode.com/datasets/license
• Add structured metadata to a dataset's meta-data page using Web standards (like schema.org and DCAT ): This allows it to be discovered and organized by anyone. A guide can be found here: https://developers.google.com/search/docs/data-types/dataset . If you use an existing data repository, this is often done automatically.
• Highly recommended: a persistent dereferenceable identifier (e.g. a DOI  minted by a data repository or a prefix on identifiers.org ) for datasets, or a code repository (e.g. GitHub, GitLab,...) for code. If this is not possible or useful, please explain why.
• For benchmarks, the supplementary materials must ensure that all results are easily reproducible. Where possible, use a reproducibility framework such as the ML reproducibility checklist , or otherwise guarantee that all results can be easily reproduced, i.e. all necessary datasets, code, and evaluation procedures must be accessible and documented.
• For papers introducing best practices in creating or curating datasets and benchmarks, the above supplementary materials are not required.
• For papers resubmitted after being retracted from another venue: a brief discussion on the main concerns raised by previous reviewers and how you addressed them. You do not need to share the original reviews.
• For the dual submission and archiving, the policy follows the NeurIPS main track paper guideline .

Use of Large Language Models (LLMs): We welcome authors to use any tool that is suitable for preparing high-quality papers and research. However, we ask authors to keep in mind two important criteria. First, we expect papers to fully describe their methodology, and any tool that is important to that methodology, including the use of LLMs, should be described also. For example, authors should mention tools (including LLMs) that were used for data processing or filtering, visualization, facilitating or running experiments, and proving theorems. It may also be advisable to describe the use of LLMs in implementing the method (if this corresponds to an important, original, or non-standard component of the approach). Second, authors are responsible for the entire content of the paper, including all text and figures, so while authors are welcome to use any tool they wish for writing the paper, they must ensure that all text is correct and original.

REVIEWING AND SELECTION PROCESS

Reviewing will be single-blind, although authors can also submit anonymously if the submission allows that. A datasets and benchmarks program committee will be formed, consisting of experts on machine learning, dataset curation, and ethics. We will ensure diversity in the program committee, both in terms of background as well as technical expertise (e.g., data, ML, data ethics, social science expertise). Each paper will be reviewed by the members of the committee. In select cases where ethical concerns are flagged by reviewers, an ethics review may be performed as well.

Papers will not be publicly visible during the review process. Only accepted papers will become visible afterward. The reviews themselves are also not visible during the review phase but will be published after decisions have been made. Authors can choose to keep the datasets themselves hidden until a later release date, as long as reviewers have access.

The factors that will be considered when evaluating papers include:

• Utility and quality of the submission: Impact, originality, novelty, relevance to the NeurIPS community will all be considered. 
• Reproducibility: All submissions should be accompanied by sufficient information to reproduce the results described i.e. all necessary datasets, code, and evaluation procedures must be accessible and documented. We encourage the use of a reproducibility framework such as the ML reproducibility checklist to guarantee that all results can be easily reproduced. Benchmark submissions in particular should take care to ensure sufficient details are provided to ensure reproducibility. If submissions include code, please refer to the NeurIPS code submission guidelines .  
• Was code provided (e.g. in the supplementary material)? If provided, did you look at the code? Did you consider it useful in guiding your review? If not provided, did you wish code had been available?
• Ethics: Any ethical implications of the work should be addressed. Authors should rely on NeurIPS ethics guidelines as guidance for understanding ethical concerns.  
• Completeness of the relevant documentation: Per NeurIPS ethics guidelines , datasets must be accompanied by documentation communicating the details of the dataset as part of their submissions via structured templates (e.g. TODO). Sufficient detail must be provided on how the data was collected and organized, what kind of information it contains,  ethically and responsibly, and how it will be made available and maintained. 
• Licensing and access: Per NeurIPS ethics guidelines , authors should provide licenses for any datasets released. These should consider the intended use and limitations of the dataset, and develop licenses and terms of use to prevent misuse or inappropriate use.  
• Consent and privacy: Per  NeurIPS ethics guidelines , datasets should minimize the exposure of any personally identifiable information, unless informed consent from those individuals is provided to do so. Any paper that chooses to create a dataset with real data of real people should ask for the explicit consent of participants, or explain why they were unable to do so.
• Ethics and responsible use: Any ethical implications of new datasets should be addressed and guidelines for responsible use should be provided where appropriate. Note that, if your submission includes publicly available datasets (e.g. as part of a larger benchmark), you should also check these datasets for ethical issues. You remain responsible for the ethical implications of including existing datasets or other data sources in your work.
• Legal compliance: For datasets, authors should ensure awareness and compliance with regional legal requirements.

ADVISORY COMMITTEE

The following committee will provide advice on the organization of the track over the coming years: Sergio Escalera, Isabelle Guyon, Neil Lawrence, Dina Machuve, Olga Russakovsky, Joaquin Vanschoren, Serena Yeung.

DATASETS AND BENCHMARKS CHAIRS

Lora Aroyo, Google Francesco Locatello, Institute of Science and Technology Austria Lingjuan Lyu, Sony AI

Contact: [email protected]

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