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Kap. 60 –Case: Proofreading

How Information Technology Is Conquering the World:

Workplace, Private Life, and Society

Professor Kai A. Olsen, Universitetet i Bergen og Høgskolen i Molde

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A semantic proofreading tool for all languages based on a text repository

Kai A. Olsen

Molde University College and Department of Informatics, University of Bergen

Norway

kai.olsen@himolde.no

Bård Indredavik

Technical Manager, Oshaug Metall AS, Molde, Norway

bard@oshaug.no

Kai A. Olsen, 20.04.23 3

Proofreading is important

When we write in a foreign language If we are not proficient in our own language To find typos and other mistakes Errors can make the text unreadable and give a very

bad impression: I am a student of MSc Logistics and Supply Chain

Management from Westminitser University, London. Last weel I had the presentation regarding Molde College University and I heart that you are the module leader of Management of value. I am wondering if you may write me back more about that module, because it not really clear for me? In particular, when I am considering to go foe the second semestr to Molde. I will be really approciate for it.

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Manual proofreading

When we are in doubt about an expression we could ask a language proficient colleague

However,we may not have anybody to ask it may be too much to ask somebody to

proofread everything that we writeCan we do it automatically?

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Automatic language processing

An important research area since the nineteen sixties The results have been far from what many envisioned Natural languages seems to be too complex to be

formalized (some argue that you have to be a human being to understand natural language)

But, due to faster computers we have workable spelling checkers and studies of syntax have offered grammar checkers that handle at least some types of mistakes

Still, clear limitations, e.g., the language tools in Office 2003 will not find these errors: “I have a red far ” ”A forest has many threes” “I live at London” ”We had ice cream for desert”

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For our student

If she had used a spelling and grammar checker in Office only a few mistakes would have been found:

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Another approach

Instead of asking another person to proofread, we could ask the whole world

That is, use the Web as a text repository and compare our sentences to those of everyone else

For example, by using Google: ”we live at the west coast” – 0 ”we live on the west coast” – 3,500,000 ”we live in the west coast” – 5,960,000

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Background paper (2004)

Journal of the American Society for Information Science and Technology, Volume 55, Issue 11, September 2004

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What if the alternatives are unknown?

We can use a wild card (*)Example: ”we live * the west coast”Study the alternatives, and check the

complete sentence with each candidate to get a frequency number

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A tedious process

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Disadvantages

A lot of workWe have to know where we are in doubtIt can be difficult to find all the

alternativesBut we can make a tool that can do this

job automatically

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Prototype

Consist of:1. A spider that collects text from the Web2. An index builder that creates an index

structure3. An analyzing program that finds

alternatives for each word in the user’s sentence

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1. Spider

Starts with a list of seeds, e.g., links to Web sites of universities, newspapers, state organizations, etc.

Retrieves text from these sites “Cleans” the text of formatting data Stores all links that are found, .html, .pdf

and .doc if these have not been encountered previously

Follows html-links recursively (we have separate spiders to parse .pdf and .doc files).

Stores the text in files, numbered consecutively.

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2. Index builder

For each word we get the files that contain at least O occurrences of the word. If O is 1 all words are included, but we may use a higher value to avoid (at least some) misspelled words.

WordWord FileFile

FileFile WordWord LinesLines

For each file we have a list of all words in the file, each word giving the lines in the file where the word occurs

All structures are represented as Boolean arrays stored as .txt files.

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In English

2.5 Gb text2,500 files (1 Mb each) for raw text200,000 words (O=10, includes only

words with a frequency of 10 or higher) and the same number of text files to show in which files the word occurs

43 million text files with line references (for each word in each file)

No problem for Windows 7

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In Norwegian

1 Gb text10,000 files (0.1 Mb each) for raw text550,000 words (O=1, all words) and the

same number of text files to show in which files the word occurs

42 million text files with line references (for each word in each file)

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3. The analyzer

Finds the frequency of the complete sentence (N words) offered by the user

Parses the files where at least N-1 words of the sentence occur

Replaces one and one word with a wild card Collects alternatives Checks the frequency of each alternative Calculates a confidence value based on the ratio of

frequencies and the similarity between the original word and the alternative (Hirschberg’s algorithm)

Suggests improvements where the alternative sentence get a higher score than the original

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Analyzer (example)

I live at London

changed to:I live in

London

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Analyzer (example 2)

We had ice cream for desert

changed to:We had ice

cream for dessert

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What kind of errors can be found

Typos, as in: I have a red far

Spelling, using the wrong word: e.g., mixing desert and dessert

Grammar, using the wrong preposition, verb, etc. e.g., mixing in/at/on/

Facts Beethoven was born in 1970 – corrected to 1770.

Punctuation That is, most types of mistakes that we make

when writing.

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When the system fails

Examples: We eat avocado, may be corrected to we eat apples Neptune is the outer planet in the solar system, may be

corrected to Pluto is the outer planet… When we have date specific data, as in the sentence “the

prime minster of Great Britain is” In practice these failures will seldom be problematic as

they often will address an area where the user is competent, also

a learning system can reduce some of these cases In addition, a system that takes dates into

consideration should help

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The prototype

Is only a prototype:1 or 2.5 Gb is not enough to get a wide

range of sentencesCatching data from the Web gives a

repository with many spelling and grammar errors (also with a lot of repeated text)

The system works too slow to handle many users

Still, it can correct many types of mistakes, e.g., all the examples that we used in our 2004 paper.

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What we need in order to

improve the text repository:A text quality checker, that ignores text with

too many errorsOr, perhaps better, text repositories based

on books, company reports, government reports, scientific papers, …

improve speed:A site with many thousands (millions) of

simple computers (i.e., a “Google” setup)The task is ideal for parallel computing

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Parallel computing: MapReduce

An algorithm offered by Dean and Ghemawat from Google

Idea – algorithms that work in parallel on large data sets

In our case: The map operation could be applied to each file,

offering the frequency of each alternative sentence (one computer can work on one file at a time)

The reduce could take these intermediate results in order to compute the final frequencies.

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Discussion

Do we want to write as the majority? Yes, when we write in a foreign language When we are not too proficient writers

Can we leave everything to the proofing tool? No, as with other type of proofing tools what we get is a suggestion

only What the tool really does is helping the user to use reading

competency when writing Will the system find examples of all sentences?

No Why do not Google and others offer this tool?

Perhaps because it will be very resource demanding (or because they are not smart enough)

What about false negatives? This (the system indicating expressions that are correct) may be a

problem.

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Conclusion

With a multicomputer setup and a large repository many mistakes can be indicated

Works in any language that can be digitized

Can be an offline or online tool (perhaps online is achievable one time in the future?)

We could have repositories that reflects style (academic, business, social…)?

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Big data is becoming important

To analyze buying patterns of customersRecommendation systemsTraffic patterns for planning new flights

or new roads (Norwegian to Molde)In science (meteorology, medicine,

physics, astronomy…)In many areas

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Data is available

From the WebFrom user actions on the Web

(keywords entered for searching, pages visited…)

From automatic sensors, modern equipment (such as better telescopes), online activities, cameras…

The computers and software are here to analyze the data

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That is

BIG DATA can be used to understand many complex processes

Will becoming an important issue in the next ten years of computing