The document discusses grammar checking and outlines techniques for identifying grammatical errors. It begins by defining what constitutes a grammatical error and discusses common error types such as subject-verb disagreement and incorrect verb subcategorization. It then provides examples of identifying various grammatical error types and how grammar checking can be used to detect and correct errors in writing.
The document discusses automated writing assistance and outlines several topics related to helping people improve their writing. It describes what writing assistance tools can help with, such as spell checking, grammar checking, and style checking. It also discusses what the course will not cover, such as handwriting or typing. The document then outlines the writing process and different ways of categorizing writing errors.
The document discusses various techniques for spell checking, including:
- Detecting spelling errors by finding words that are not in the dictionary or have an edit distance of 1 from real words.
- Generating candidate corrections by considering words within an edit distance of 1.
- Ranking candidate corrections using methods like trigram analysis, error pattern analysis, triphone analysis, and noisy channel modeling.
- Techniques have improved over time from using word lists and edit distance to utilizing linguistic knowledge about common error types.
Fixing the program my computer learned: End-user debugging of machine-learned...City University London
This document summarizes Dr. Simone Stumpf's research into enabling end users to debug machine-learned programs. It discusses how machine-learned programs work and the challenges end users face in debugging programs they can't see the source code of. It describes formative studies exploring different explanation approaches and the types of feedback users provide. It also covers integrating user feedback to change the machine's reasoning, identifying unpredictable user-provided features, and directions for future work.
The document provides an introduction to the concept of plain language, including its goals of making information easy for audiences to find, understand and use. It discusses the benefits of plain language for both authors and audiences in terms of time, money and compliance savings. The document also outlines techniques for writing in plain language and common habits to avoid, and provides an overview of the 2010 Plain Writing Act requirements for federal agencies to use plain language in documents for the public.
Techniques for automatically correcting words in textunyil96
The problem of automatically correcting words in text has been an ongoing research challenge since the 1960s. Existing spelling checkers and text recognition techniques are limited in their accuracy. Three main areas of research have focused on detecting and correcting (1) nonwords, (2) isolated misspelled words, and (3) context-dependent real-word errors. While progress has been made, fully automatic correction of all word errors requires techniques that can analyze contextual information to detect errors resulting in other valid words.
The document provides guidance on using plain language when communicating complex information to general audiences. It discusses several techniques for testing language such as speaking to the intended audience, avoiding jargon, using common words with common meanings, knowing which grammar rules are most important for clarity, being precise, promoting descriptions over exceptions, avoiding hidden verbs, being actionable, and using visual aids. The goal is to provide concise yet comprehensive summaries.
There are good reasons to focus on analyzing learner errors, including that errors reveal gaps in a learner's knowledge and help teachers understand what learners are struggling with. The document outlines several key steps and considerations for error analysis: identifying errors by comparing learner language to correct target language, distinguishing between errors and mistakes, classifying errors into grammatical categories, describing how learner utterances differ from the target language, and explaining error types such as omission, overgeneralization, and transfer from the first language. Developmental patterns in learner language are also discussed, showing that acquisition involves transitional constructions and varies based on linguistic, situational, and psycholinguistic contexts.
The document discusses automated writing assistance and outlines several topics related to helping people improve their writing. It describes what writing assistance tools can help with, such as spell checking, grammar checking, and style checking. It also discusses what the course will not cover, such as handwriting or typing. The document then outlines the writing process and different ways of categorizing writing errors.
The document discusses various techniques for spell checking, including:
- Detecting spelling errors by finding words that are not in the dictionary or have an edit distance of 1 from real words.
- Generating candidate corrections by considering words within an edit distance of 1.
- Ranking candidate corrections using methods like trigram analysis, error pattern analysis, triphone analysis, and noisy channel modeling.
- Techniques have improved over time from using word lists and edit distance to utilizing linguistic knowledge about common error types.
Fixing the program my computer learned: End-user debugging of machine-learned...City University London
This document summarizes Dr. Simone Stumpf's research into enabling end users to debug machine-learned programs. It discusses how machine-learned programs work and the challenges end users face in debugging programs they can't see the source code of. It describes formative studies exploring different explanation approaches and the types of feedback users provide. It also covers integrating user feedback to change the machine's reasoning, identifying unpredictable user-provided features, and directions for future work.
The document provides an introduction to the concept of plain language, including its goals of making information easy for audiences to find, understand and use. It discusses the benefits of plain language for both authors and audiences in terms of time, money and compliance savings. The document also outlines techniques for writing in plain language and common habits to avoid, and provides an overview of the 2010 Plain Writing Act requirements for federal agencies to use plain language in documents for the public.
Techniques for automatically correcting words in textunyil96
The problem of automatically correcting words in text has been an ongoing research challenge since the 1960s. Existing spelling checkers and text recognition techniques are limited in their accuracy. Three main areas of research have focused on detecting and correcting (1) nonwords, (2) isolated misspelled words, and (3) context-dependent real-word errors. While progress has been made, fully automatic correction of all word errors requires techniques that can analyze contextual information to detect errors resulting in other valid words.
The document provides guidance on using plain language when communicating complex information to general audiences. It discusses several techniques for testing language such as speaking to the intended audience, avoiding jargon, using common words with common meanings, knowing which grammar rules are most important for clarity, being precise, promoting descriptions over exceptions, avoiding hidden verbs, being actionable, and using visual aids. The goal is to provide concise yet comprehensive summaries.
There are good reasons to focus on analyzing learner errors, including that errors reveal gaps in a learner's knowledge and help teachers understand what learners are struggling with. The document outlines several key steps and considerations for error analysis: identifying errors by comparing learner language to correct target language, distinguishing between errors and mistakes, classifying errors into grammatical categories, describing how learner utterances differ from the target language, and explaining error types such as omission, overgeneralization, and transfer from the first language. Developmental patterns in learner language are also discussed, showing that acquisition involves transitional constructions and varies based on linguistic, situational, and psycholinguistic contexts.
IRJET- Querying Database using Natural Language InterfaceIRJET Journal
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This document summarizes a presentation about a sentiment analysis system developed for a large Korean telecommunications company. The system was designed to analyze customer feedback from call centers. It classified feedback into categories, identified trends over time, and detected complaints. The system used Korean linguistic analysis and sentiment classification. It showed the benefits of combining machine learning and rules-based approaches. However, challenges remained around data quality, lexicon development, and meeting customer expectations. Future work focused on improving the sentiment dictionary and developing a platform for ongoing natural language processing services.
Using construction grammar in conversational systemsCJ Jenkins
This thesis explored using construction grammar and ontologies in conversational systems. The author built two early experimental systems using these techniques. Construction grammar represents language as constructions pairing form and meaning. Ontologies allow for more explicit semantics compared to databases. The author developed a stemmer called UEA-Lite and a system called KIA that incorporated construction grammar, ontologies, and machine learning to understand and respond to natural language.
This document discusses different types of errors in programming:
1. It covers syntax errors, runtime errors, logic errors, incorrect operator precedence errors, and threading errors as sources of errors.
2. It provides examples of each type of error, such as division by zero for runtime errors and wrong scoping of variables for logic errors.
3. The document concludes with asking readers to discuss common errors they have encountered and providing a short quiz question about error types.
DevOps Enterprise Summit 2019 - How Swarming Enables EnterpriseSupport to wo...Jon Stevens-Hall
This document discusses how swarming enables enterprise support to better work with DevOps. It describes how swarming involves removing support tiers and instead calling on the collective expertise of an analyst "swarm". The document outlines BMC's swarming process for handling support issues and provides examples of improved results like reduced resolution times. It argues that swarming aligns well with DevOps principles and can help address challenges in complex systems using a Cynefin framework approach of probing, sensing, and responding.
Presented by Marc Krellenstein | Lucid Imagination http://www.lucidimagination.com/devzone/events/conferences/revolution/2011
While it remains challenging to build best practice search applications, core search technology has become commoditized. Open source Lucene/Solr represents the best form of that commodity. It is as good as or better than than any commercial search technology while also providing the cost, control and flexibility advantages of open source. In this talk, we'll look at how past challenges in search were met and new ones evolved, and the place of Lucene/Solr in that evolution.
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This document provides a summary of error analysis and its historical background. It discusses how error analysis evolved from contrastive analysis in the 1960s. Contrastive analysis predicted errors based on differences between a learner's native language and the target language but did not accurately predict all errors. Error analysis emerged in the 1970s as a superior alternative that studied all types of learner errors without relying solely on native language influences. The document outlines the typical steps in conducting an error analysis, including collecting language samples, identifying errors, describing errors, and explaining error sources. It also discusses theoretical perspectives like interlanguage theory and different types of errors learners may make. Finally, it notes that error evaluation was a supplementary step to determine which errors required instruction but
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This document summarizes a student project on mining user opinions from hotel reviews. It discusses using data mining techniques like machine learning and sentiment analysis to analyze large amounts of online hotel review data and identify useful patterns. Specifically, it aims to predict review sentiment polarity, classify words by polarity in a sentiment lexicon, and detect relations between aspects and sentiments. The challenges include limitations of current sentiment lexicons and algorithms in capturing domain and context dependencies. The student proposes expanding existing lexicons using rule-based mining to help improve sentiment analysis accuracy.
The document discusses metalanguage, which refers to an awareness of one's own language use. It explains that metalanguage involves understanding grammar rules to be able to identify errors and explain reasons for errors. Developing metalanguage over time through consistent practice helps improve language proficiency and the ability to self-correct. The document provides examples and an activity to help build metalanguage skills.
This document provides an overview of software testing and debugging. It discusses the definitions and purposes of testing and debugging. Testing is the process of verifying that a system meets specified requirements, while debugging is the process of finding and fixing errors in source code. The document then covers various topics related to software testing such as the phases of a tester's work, the goals and dichotomies of testing versus debugging, models for testing, consequences of bugs, taxonomies of bugs, and test metrics.
Define what language is;
Examine the early linguistic approaches to SLA: Contrastive Analysis , Error Analysis Interlanguage , Morpheme Order Studies, and Monitor Model;
Bring the internal focus with up-to-date discussion of Universal Grammar (UG): what constitutes the language faculty of the mind;
Discuss external focus: the functions of language that emerge in the course of second language acquisition Systemic Linguistics, Functional Typology , Function-to- Form Mapping , and Information Organization.
Applied the learned knowledge in the language classroom.
The document discusses the challenges of vocabulary alignment between ontologies. It notes that current systems use complex reasoning that does not scale well to large vocabularies and makes results difficult to explain. While alignment failures occur for different reasons each time, the document argues that interactive alignment involving domain experts can address this problem and be applied successfully even to large datasets. It suggests that the current evaluation protocol does not adequately assess interactive features or account for the human roles involved in ontology development and use. An example alignment between the AAT and WordNet ontologies is provided.
The document discusses analyzing communicative events to effectively assess language learning needs. It suggests identifying common functions, vocabulary overlap, and skills across events to prioritize training objectives. A case study examines one learner's job duties and results in a scheme of work focusing on describing problems, processes, and past actions to improve his work performance. The analysis fits materials to learners' needs rather than fitting learners to materials.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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2. It provides examples of each type of error, such as division by zero for runtime errors and wrong scoping of variables for logic errors.
3. The document concludes with asking readers to discuss common errors they have encountered and providing a short quiz question about error types.
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This document summarizes a student project on mining user opinions from hotel reviews. It discusses using data mining techniques like machine learning and sentiment analysis to analyze large amounts of online hotel review data and identify useful patterns. Specifically, it aims to predict review sentiment polarity, classify words by polarity in a sentiment lexicon, and detect relations between aspects and sentiments. The challenges include limitations of current sentiment lexicons and algorithms in capturing domain and context dependencies. The student proposes expanding existing lexicons using rule-based mining to help improve sentiment analysis accuracy.
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HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
1. Automated Writing Assistance:
Grammar Checking and Beyond
Topic 3: Grammar Checking
Robert Dale
Centre for Language Technology
Macquarie University
SSLST 2011 1
3. Outline
• What is a Grammatical Error?
• Grammar Checking without Syntax
• IBM’s EPISTLE
• Grammar Checking Techniques
• Related Areas
• Commercial Packages
SSLST 2011 3
4. What is a Grammatical Error?
• Something that breaks the rules of the language
• Who decides?
– Dialects
– Formality
– Language change
• Some jurisdictions are stricter than others
– L'Académie française and its 40 ‘immortals’
SSLST 2011 4
6. Taxonomies of Error:
Douglas and Dale 1991
Number Disagreement
Bad Subcategorisation
Resumptive Pronoun
Spelling Errors Syntactic Parallelism
Co-occurrence Errors
Syntactic Errors
Dependency and Subordination Errors
Semantic Problems
Bad Clause Conjunction
Stylistic Problems
Misleading PP Attachment
Rhetorical Problems
Misleading Adverbial Attachment
Punctuation Problems
Missing Subordination Indicator
SSLST 2011
Redundancy 6
7. Subject–Verb Number Disagreement
• But the males in this study experienced significant difficulties in
this area and this problem suggest that some more attention be
paid to the phenomenon.
• This method requires a user to think aloud while performing a
task, while the researchers makes notes, and perhaps records
the session on audio or video tape.
• The main reported problems was the Unix editor vi.
SSLST 2011 7
8. Subject–Verb Number Disagreement
• But the males in this study experienced significant difficulties in
this area and this problem suggest that some more attention be
paid to the phenomenon.
• This method requires a user to think aloud while performing a
task, while the researchers makes notes, and perhaps records
the session on audio or video tape.
• The main reported problems was the Unix editor vi.
The main reported problems were with the Unix editor vi.
SSLST 2011 8
9. Incorrect Subcategorisation Frames:
Verbs
• Both Carroll’s work and our own, however, has tended to use
existing commercial manuals as a basis --- and the question
then is how to prune to a fraction of their original size, and to
alter their contents to approach more closely to the problems
that users actually confront when trying to learn a new system.
SSLST 2011 9
10. Incorrect Subcategorisation Frames:
Verbs
• Both Carroll’s work and our own, however, has tended to use
existing commercial manuals as a basis --- and the question
then is how to prune to a fraction of their original size, and to
alter their contents to approach more closely to the problems
that users actually confront when trying to learn a new system.
SSLST 2011 10
11. Incorrect Subcategorisation Frames:
Nouns and Prepositions
• Their feedback pointed to problem areas and causes for
misinterpretation, and suggestions of improvements offered by
them.
SSLST 2011 11
12. Incorrect Subcategorisation Frames:
Nouns and Prepositions
• Their feedback pointed to problem areas and causes for
misinterpretation, and suggestions of improvements offered by
them.
Their feedback pointed to problem areas and causes of
misinterpretation, and suggestions for improvements offered
by them.
SSLST 2011 12
13. Incorrect Subcategorisation Frames:
Verbs
• In this way, it is anticipated that the issue of native users not
really knowing what it is they need to know is dealt with.
SSLST 2011 13
14. Incorrect Subcategorisation Frames:
Verbs
• In this way, it is anticipated that the issue of native users not
really knowing what it is they need to know is dealt with.
In this way, it is anticipated that the issue of native users not
really knowing what it is they need to know will be dealt with.
SSLST 2011 14
16. Incorrect Subcategorisation Frames:
Nouns and Prepositions
• All mailing systems have capabilities of composing, sending
and receiving messages.
All mailing systems have facilities for composing, sending and
receiving messages.
SSLST 2011 16
18. Incorrect Subcategorisation Frames:
Adjectival Complements
• The feature checklist was easy to administer and complete by
experienced users …
The feature checklist was easy to administer and easy for
experienced users to complete …
SSLST 2011 18
19. Syntactic Parallelism Failures
• Semi-structured interviews were conducted with experienced
users to find what their most common tasks, the tasks a new
user would need to begin, and what errors would be most likely
in the early stages.
SSLST 2011 19
20. Syntactic Parallelism Failures
• Semi-structured interviews were conducted with experienced
users to find what their most common tasks, the tasks a new
user would need to begin, and what errors would be most likely
in the early stages.
Semi-structured interviews were conducted with experienced
users to find what their most common tasks were, what tasks a
new user would need to begin, and what errors would be most
likely in the early stages.
SSLST 2011 20
21. Bad Clause Conjunction
• It had approximately 13% of the pages of the commercial
manual, it allowed 30% faster learning and more effective use
of the email system overall, and significantly better
performance on individual subtasks including recovery from
error.
SSLST 2011 21
22. Bad Clause Conjunction
• It had approximately 13% of the pages of the commercial
manual, it allowed 30% faster learning and more effective use
of the email system overall, and significantly better
performance on individual subtasks including recovery from
error.
It had approximately 13% of the pages of the commercial
manual, it allowed 30% faster learning and more effective use
of the email system overall, and it gave significantly better
performance on individual subtasks including recovery from
error.
SSLST 2011 22
23. Bad Clause Conjunction
• The conditions under which our subjects worked tended to
minimize such problems – since we asked them to persevere,
and in the end they would be able to get human help.
SSLST 2011 23
24. Bad Clause Conjunction
• The conditions under which our subjects worked tended to
minimize such problems – since we asked them to persevere,
and in the end they would be able to get human help.
The conditions under which our subjects worked tended to
minimize such problems, since we asked them to persevere,
and in the end they would be able to get human help.
SSLST 2011 24
25. Bad Clause Conjunction
• The more active but ineffectual behaviour of the males may
mean that they feel they must be capable of mastering the
system, of overcoming their errors and are less worried or
affected by the possibility of making errors.
SSLST 2011 25
26. Bad Clause Conjunction
• The more active but ineffectual behaviour of the males may
mean that they feel they must be capable of mastering the
system, of overcoming their errors and are less worried or
affected by the possibility of making errors.
The more active but ineffectual behaviour of the males may
mean that they feel they must be capable of mastering the
system and of overcoming their errors, and are less worried or
affected by the possibility of making errors.
SSLST 2011 26
27. Bad Clause Conjunction
• Novice users should, however, be able to voice thoughts and
desires on any topic, throughout the process if the manual is to
be properly user-centred.
SSLST 2011 27
28. Bad Clause Conjunction
• Novice users should, however, be able to voice thoughts and
desires on any topic, throughout the process if the manual is to
be properly user-centred.
However, if the manual is to be properly user-centred, novice
users should be able to voice thoughts and desires on any
topic throughout the process.
SSLST 2011 28
29. Syntactic Redundancy
• So although this seems to be is a winning feature in learning, it
may not …
• … this problem suggests that some more attention be paid to
the phenomenon
• … thus so this argues for the complementary use of …
SSLST 2011 29
30. Syntactic Redundancy
• So although this seems to be is a winning feature in learning, it
may not …
• … this problem suggests that some more attention be paid to
the phenomenon
• … thus so this argues for the complementary use of …
SSLST 2011 30
31. What Causes Grammar Errors?
• Competence-based errors:
– Unfamiliarity with the language
• Performance-based errors:
– Repeated words
– Editing errors
SSLST 2011 31
32. Outline
• What is a Grammatical Error?
• Grammar Checking without Syntax
• IBM’s EPISTLE
• Grammar Checking Techniques
• Related Areas
• Commercial Packages
SSLST 2011 32
33. The Unix Writer’s Workbench
• A breakthrough in the early 1980s
– We believe that the Writer's Workbench programs provide a
more general text analysis system than JOURNALISM or
CRES, and unlike EPISTLE they are already in wide use. At
Bell Laboratories there are over 1000 users on over 50
machines. [1982:106]
• Widely-used in educational contexts
• Underlying technology formed the basis for the first PC
grammar checkers: Grammatik, RightWriter, StyleWriter
SSLST 2011 33
34. The Unix Writer’s Workbench:
Proofreading with PROOFR
• Checks for existence of non-word spelling errors; user-specified
automatic correction can be carried out
• Checks for unbalanced punctuation and other simple
punctuation mistakes
• Checks for double words
• Checks for misused words, wordy phrases, sexist terms, …
• Checks for split infinitives using a simple PoS tagger
SSLST 2011 34
35. The Unix Writer’s Workbench:
Stylistic Analysis with STYLE
• Based on PoS tagging, provides 71 numbers describing stylistic
features of the text
– Readability indices
– Average sentence and word length
– Distribution of sentence lengths
– Percentage of verbs in passive voice
– Percentage of nouns that are nominalisations
–…
SSLST 2011 35
36. The Unix Writer’s Workbench:
Stylistic Analysis with STYLE
SSLST 2011 36
37. The Unix Writer’s Workbench:
Other Components
• PROSE: compares the stylistic parameters of a given text
against a domain-specific standard
• ABST: determines the conceptual abstractness of a text via a list
of 314 abstract words
• ORG: prints only first and last sentences of paragraphs
SSLST 2011 37
38. Atwell [1987]:
CLAWS
• Originally built to assign PoS tags to the London-Oslo-Bergen
corpus
• Developed in part because of the computational cost of more
complex systems:
– ‘[Heidorn et al 82] reported that the EPISTLE system
required a 4Mb virtual machine (although a more efficient
implementation under development should require less
memory).’ [1987:38]
SSLST 2011 38
39. Atwell [1987]:
Constituent-Likelihood Error Detection
• For PoS tagging, uses a table of PoS bigram frequencies to
determine most likely sequences
• Detects grammatical errors by flagging unlikely PoS transitions
• Doesn’t need separate data for training error likelihoods
SSLST 2011 39
40. Outline
• What is a Grammatical Error?
• Grammar Checking without Syntax
• IBM’s EPISTLE
• Grammar Checking Techniques
• Related Areas
• Commercial Packages
SSLST 2011 40
41. IBM’s EPISTLE:
History
• Initial work in the early 1980s led to several innovative
techniques
• Based on Heidorn’s Augmented Phrase Structure Grammar
[1975]
• Renamed CRITIQUE somewhere in the mid to late1980s
• Released on IBM mainframes late 1980s
• Key team members went on to build Microsoft Word’s grammar
checker from 1992 onwards
• Grammar checking released as part of MS Word 97
SSLST 2011 41
42. IBM’s CRITIQUE:
Grammar vs Style
• Grammatical critiques:
– Strict rules as to whether a sentence is grammatical or not
– Correction is typically clear
• Stylistic weaknesses are less black and white:
– too great a distance between subject and verb
– too much embedding
– unbalanced subject/predicate size
– excessive negation or quantification
–…
SSLST 2011 42
43. IBM’s CRITIQUE :
Grammar Errors
• Number Disagreement:
– he go, many book, it clarifies and enforce
• Wrong Pronoun Case:
– between you and I, it is me
• Wrong Verb Form:
– had expect, seems to been
• Punctuation:
– run-on sentences, questions with a final period instead of a question mark
• Confusions:
– who’s vs whose, it’s vs its, your vs you’re, form vs from
SSLST 2011 43
44. IBM’s CRITIQUE :
Stylistic Weaknesses #1
• Excessive length
– Sentences or lists that are too long
– Sequences with too many prepositional phrases
• Excessive complexity
– Noun phrases with too many premodifiers
– Clauses with a series of ands
– Verb phrases with too many auxiliary verbs
– Clauses with too much negation
• Lack of parallelism
– Example: you should drink coffee rather than drinking tea
SSLST 2011 44
45. IBM’s CRITIQUE :
Stylistic Weaknesses #2
• Excessive formality
– phrases that are bureaucratic, pompous or too formal
• Excessive informality
– constructions acceptable in spoken English but too informal when written
• Redundancy
– phrases that can be shortened without loss in meaning
• Missing punctuation
• Nonpreferred constructions
– Split infinitives [eg to completely remove], colloquial usage [eg ain’t working]
SSLST 2011 45
46. The MS Word Grammar Checker:
Processing Steps
1. Tokenisation and Lexical Lookup
2. Syntactic Sketch
3. Syntactic Portrait
4. Production of Logical Forms
SSLST 2011 46
47. The MS Word Grammar Checker:
An Example
• Consider the following sentence:
– After running a mile he seemed tired.
SSLST 2011 47
48. The MS Word Grammar Checker:
Lexical PoS Records
• Also includes detection of
multiword elements and named
entity mentions
• Lexicon based on LDOCE and AHD
+ supplementary information
added both manually and
automatically
• Over 100k words
• There are two other records
produced for ‘after’ here for the
Adj and Adv uses
SSLST 2011 48
49. The MS Word Grammar Checker:
Syntactic Analysis
• Bottom-up chart parser
• Uses probabilities and
heuristics
• Grammar contains 125
mostly binary rules
• This is the derivation tree
SSLST 2011 49
50. The MS Word Grammar Checker:
Syntactic Analysis
SSLST 2011 50
51. The MS Word Grammar Checker:
Syntactic Information Stored at the Root Node
SSLST 2011 51
55. The MS Word Grammar Checker:
A Segment Record with An Error
SSLST 2011 55
56. The MS Word Grammar Checker:
The Results of Error Checking
SSLST 2011 56
57. The MS Word Grammar Checker:
Controlling the Checker’s Behaviour
SSLST 2011 57
58. EPISTLE/CRITIQUE/MS Word:
Key Ideas
• A metric for ranking alternative parses [Heidorn 1982]
• Relaxation for parsing errorred sentences [Heidorn et al 1982]
• A heuristic fitted parsing technique for sentences outside the
grammar’s coverage [Jensen et al 1983]
SSLST 2011 58
59. Outline
• What is a Grammatical Error?
• Grammar Checking without Syntax
• IBM’s EPISTLE
• Grammar Checking Techniques
• Related Areas
• Commercial Packages
SSLST 2011 59
60. Constraint Relaxation:
The Basic Idea
• When a sentence cannot be parsed, relax the grammar rules in
some way so that it can be parsed
• The particular constraints that are relaxed indicate what the
nature of the grammatical error is
• First explored in the context of robust parsing by Weischedel
and Black [1980]
SSLST 2011 60
61. Constraint Relaxation:
Handling Constraint Violation Errors
• Subject-verb number agreement
* John and Mary runs
• Premodifier-noun number agreement
* This dogs runs
• Subject-complement number agreement
* There is five dogs here
• Wrong pronoun case
* He and me ran to the door
• Wrong indefinite article
* A apple and a rotten old pear.
SSLST 2011 61
64. Constraint Relaxation
• Advantages:
– provides a precise and systematic way of specifying the
relationship between errorful and ‘correct’ forms, making it
easier to generate suggestions for corrections
• Disadvantages:
– Requires significant amounts of hand-crafted linguistic
knowledge
SSLST 2011 64
65. Mal-Rules
• Also known as error anticipation
• Mal-rules explicitly describe specific expected error forms
SSLST 2011 65
66. A Mal-Rule for Handling Omissions
[Schneider and McCoy 1998]
• Example:
The boy happy
• Conventional rule:
VP V AdjP
• Malrule:
VP[error +] AdjP
SSLST 2011 66
67. Mal-Rules
• Advantage:
– Specifically targets known problems
– Allows easy identification of the nature of the error
• Disadvantages:
– Requires error types to be catalogued in advance
– Infeasible to anticipate every possible error
• Arguably mal-rules are just a notational variant of constraint
relaxation approaches
SSLST 2011 67
68. Other Approaches
• Fitted parsing [Jensen et al 1983]
• Mixed bottom-up and top-down parsing [Mellish 1989]
• Minimum edit distance parsing [Lee et al 1995]
SSLST 2011 68
69. Outline
• What is a Grammatical Error?
• Grammar Checking without Syntax
• IBM’s EPISTLE
• Grammar Checking Techniques
• Related Areas
• Commercial Packages
SSLST 2011 69
70. Robust Parsing
• The Goal:
– Analyse extragrammatical input in order to extract some
useful meaning
• No need to characterise and repair the error
• Processing of spoken language is a special case
SSLST 2011 70
71. Controlled Languages
• The Goal:
– Ensure that a text conforms to a specific set of rules and
conventions
• Examples:
– ASD Simplified Technical English
– Caterpillar Technical English
– EasyEnglish
– Attempto Controlled English
• See http://www.geocities.ws/controlledlanguage/
SSLST 2011 71
72. Outline
• What is a Grammatical Error?
• Grammar Checking without Syntax
• IBM’s EPISTLE
• Grammar Checking Techniques
• Related Areas
• Commercial Packages
SSLST 2011 72
73. Do Current Grammar Checkers Help?
• In real use, grammar checkers may have low recall and low
precision
SSLST 2011 73
74. Kohut and Gorman [1995]:
An Empirical Evaluation of Five Packages
Package Total # Real Errors Real Errors False Errors False
Errors Correctly Incorrectly Errors/Total
Identified Identified Deteted
PowerEdit 133 47% 12% 11% 16.13%
RightWriter 133 34% 8% 7% 13.85%
Grammatik 133 31% 6% 11% 23.44%
Editor 133 17% 3% 4% 16.13%
CorrectGrammar 133 15% 5% 10% 32.5%
SSLST 2011 74
75. Kohut and Gorman [1995]:
An Empirical Evaluation of Five Packages
SSLST 2011 75
86. Conclusions
• Grammar checking is hard even for humans
• Automated grammar checking is a very unsolved problem
• Grammar checking is not necessarily distinct from spelling
checking and style checking
• Many of the problems in real texts are more complex than
straightforward textbook grammar errors, and often co-occur
with other errors
• There’s lots to be done!
SSLST 2011 86