What? Investigating what a corpus is about Max Kemman University of - - PowerPoint PPT Presentation

What? Investigating what a corpus is about

Max Kemman

University of Luxembourg October 25, 2015

Doing Digital History: Introduction to Tools and Technology

Recap from last time

What is distant reading? What is an n-gram? What do the Y-axis and X-axis show?

Recap - Assignment

How did the assignment go? What did you think of the tools used? Could this be useful for your research?

One more thing on HTML: special characters

http://www.ascii.cl/htmlcodes.htm Find the symbol and the HTML number é & ü -> & é & ü -> é & ü In your HTML, write longue durée to write longue durée

One more thing: what is an algorithm?

A set of rules to follow to solve a problem Pretty much like a cooking recipe

a = 0 while(a < 10) { a = a + 1 }

Today

The W's of research

• What a corpus is about
• The entities in a corpus
• Another look at our emails
• Voyant Tools
• Next time
• Assignment

The W's of research

Thus far: Now: we have a digital corpus, what to do with it?

• 1. Abundance of sources
• 2. Writing for the Web
• 3. Digitisation and Digital Libraries
• 4. Big Data
• 5. Distant Reading

Research the corpus

Now come the W's of research:

• 1. What - Investigating what a corpus is about
• 2. Where - Investigating the spatial entities in a corpus
• 3. When - Investigating the temporal entities in a corpus
• 4. Who - Investigating the social entities in a corpus

What?

The first W of interest, what is this corpus actually about? Different methods are possible

Find a description of the corpus to read

• Select a sample of documents to read
• Visualize the used words

What a corpus is about

What is this conference about?

Word clouds

Advantages of word clouds

Very easy to create

• Visually pleasing
• Gives a quick overview

What does a word cloud do?

Put very simply, a word cloud does the following:

• 1. Count the number of occurrences per word
• 2. Size each word by its frequency
• 3. Layout the words to form a shape
• 4. Optional: colorize words for distinguishing and better readability

Layout

Unlike the Ngram viewer: no X or Y axes The position of each word is meaningless The meaning is in the size of the words

Counting

Word clouds visualize the frequency of words But how to count words that vary in spelling?

E.g. "Digital" and "digital" and "digitally", "digitize" and "digitization"

• Normalization:

Lowercase

• Tokenize
• Stemming or lemmatizing
• Stopwords

Lowercase We were on vacation in France in August 2015 we were on vacation in france in august 2015

Tokenize we were on vacation, in france, in august 2015 we|were|on|vacation|in|france|in|august|2015

Stemming or lemmatizing digitized|digital|digitization|digitizing Stemming: digit Lemmatizing: digitiz|digital Could be very useful especially with Latin texts

Stopwords Most common words in the language: and, or, the Sometimes: remove numbers Not of interest (usually) we|were|on|vacation|in|france|in|august|2015 we|were|vacation|france|august|

What are these grants about? (normalized)

Comparing between different parts of the corpus

Sources separated by their citation behaviour

Representing a model of the text

What if we do not know how to separate sources? Or if we want to know what other words are related to our keywords?

Topic modelling Documents and words can be directly observed, but topics are latent How to represent the topics in a corpus?

(Slides on topic modelling from Pim Huijnen and Marijn Koolen)

Statistics to find topics represented by groups of words

• Document is a mix of topics
• Topic is a mix of words

Topic modelling Assumption: two documents with the same topics will have overlap in words For a given corpus, modelling process does:

• 1. Create word probability distribution for topics
• 2. Create topic probability distribution for documents

Topic modelling In short: a corpus is represented by statistical topics This allows us to:

Separate sources by topics

• Find related keywords

Comparing different parts of the corpus Mendeley Research Maps Comparing the topical similarity Assigned documents to disciplines to map disciplines by topics Which form of machine learning would this be?

What is the corpus about?

We can now represent the words or the topics of a corpus But, remember: World War I ≠ "World War I"

The entities in a corpus

Thus far we know the frequencies of all the words But what are we interested in? What do we need for the other W's?

The entities in a corpus

Thus far we know the frequencies of all the words But what are we interested in? What do we need for the other W's?

Where - places

• When - dates
• Who - people

People in the corpus

Ter Braake & Fokkens - Fairly easy to discover famous people (with biographical dictionaries and Ngram viewers) Ngrams help top-down: when you know who to search for But how to discover who did not become famous, while prominent in their

• wn time?

Need to find all people bottom-up by identifying all the names

Bottom-up proces

Ter Braake & Fokkens

• 1. Identify all names in the corpus
• 2. Give all names an identifier
• 3. Disambiguate names referring to the same person
• 4. Compare results with a non-digital corpus
• 5. Visualize the results
• 6. Interpret!

Identifying names Combinations of words that start with a capital This won't work for German Their algorithm allows for two sequential lower case words: Johan van der Capellen Note: built for recall, not precision

Recall & Precision Recall: retrieve all relevant entities Precision: do no retrieve irrelevant entities For algorithms usually a choice what to optimize

Recall of people referred to with single name (Erasmus, Rembrandt) would lead to too much noise = lower precision

Difficulties

Spelling of names (especially before 19th century) People with the same name Nicknames and changing names People with the same title Context matters!

Named Entity Recognition

We want to identify the entities We were on vacation in France in August 2015. I went to shop at the

• Intermarche. The area around Apt is really nice. Max also bought icecream,

which cost €2. We were on vacation in France in August 2015. I went to shop at the

• Intermarche. The area around Apt is really nice. Max also bought icecream,

which cost €2.

Named Entities

Or we want to see: We were on vacation in France in August 2015. I went to shop at the

• Intermarche. The area around Apt is really nice. Max also bought icecream,

which cost €2.

People: Max

• Places: France, Apt
• Organizations: Intermarche
• Dates: August 2015
• Currencies: €2

Another look at our emails

For all 30k emails, we performed text normalisation and named entity recognition Let's take a look at https://www.wikileaks.org/clinton-emails/emailid/8 Exercise 1: try to normalise the text Exercise 2: try to discover the named entities: People, Places, Organisations

Normalised

See Email8-normalised.txt in Moodle under "Emails" unclassife, us, department, state, case, f--, doc, date, release, full, hrod, clintonemailcom, sent, friday, july, pm, sullivanjj, stategov, subject, re, pakistan, bomb, ok, go, original, message, sullivan, jacob, sullivanjj, stategov, sent, fri, jul, subject, pakistan, bomb, fyi, put, follow, statement, statement, secretary, clinton, bomb, shrine, sy, ali, hujviri, lahore, shock, sadden, yesterday, attack, one, pakistan, popular, place, worship, shrine, sy, ali, hujviri, data, ganjbakhsh, lahore, claime, live, many, innocent, pakistane, extremist, shown, respect, neither, human, dignity, fundamental, religious, value, pakistani, society, violact, sanctity, rever, shrine, particularly, sinister, attempt, destabilize, pakistan, intimidate, people, attacker, will, succeed, pakistani, public, refuse, cow, violence, condemn, brutal, crime, reaffirm, commitment, support, pakistani, people, effort, defend, democracy, violent,

commitment, support, pakistani, people, effort, defend, democracy, violent, extremist, seek, destroy, thought, prayer, family, victim, people, pakistan

Named Entities

Try to do it by hand NER tool: http://nlp.stanford.edu:8080/ner/

People Places Organisations Sullivan Jacob CLINTON Ali Hujviri Pakistan Pakistan Lahore Pakistan Lahore Pakistan Pakistan U.S. Department of State Case No Shrine of Syed Ali Hujviri

Visualise the email

Go to http://tagcrowd.com/ Compare with and without stopwords Compare normal and normalised text

What?

So, what's the email about? Do we get different perspectives?

Voyant Tools

Go to www.voyant-tools.org/ Use Mozilla Firefox, it doesn't work in Chrome (that's what went wrong during lecture) From Moodle: download the files for emails 6000-6019 f6-20-raw.txt and f6-20-normalised.txt You can paste in text, or upload the file Continue by hitting reveal

Saving the Voyant session It might be a good idea to copy the URL early on, as this will allow you to refresh the page if the tool crashes, or to open the tool again later on using the data and stopwords you already had Share the session: mousehover the top blue bar, and click the third icon in the topright (see image), you can then choose to share the URL: this will open a new browser window where you can copy the address from

Voyant windows Look at all the windows in Voyant and see if you understand them

• 1. Cirrus (word cloud)
• 2. Reader
• 3. Summary
• 4. Trends
• 5. Contexts

Voyant Word Clouds

In the Cirrus, hold mouse on the title bar, and click 3rd icon

• Select the stopword list you need
• Or Edit List to add more words: 1 word per line, click Save
• Check apply globally to activate in all windows
• Use the word cloud to detect common words we're not interest in: unclassified,

department, subject, etc

• Hit Confirm
• When editing again, the stopwords are ordered alphabetically, so you might not see them

at the end anymore

Voyant Summary What is the longest email? What are distinctive words? Distinctive words calculated by TF-IDF: what was that again? Update: the distinctive words feature doesn't work now that we combined all the emails in a single text-file

Searching specific words In the Cirrus window, you can click Terms in the top bar to get the list of words ordered by count You can see immediately per word how it develops over time in the emails From this list you can select a word by checking the box left to it Alternatively, you can search for words per window. For example, in the Contexts window (lower-right), at the bottom is a search box where you can search for words

Interpreting with Voyant What are the biggest words? How do they develop throughout the emails? Does this tell what the emails are about and how it goes? If not: what is different?

Sharing the Voyant You can either

Take screenshots of what you want to show

• Share the session: mousehover the top blue bar, and click the third icon in the topright

(see image), you can then choose to share the URL: this will open a new browser window where you can copy the address from

• The HTML snippet will give an HTML code that you can embed in your report.
• Share specific windows: for example, in the top bar of trends, click the first icon (see

image), and select to export a url, a HTML snippet for embedding, or a PNG for including in your report

Next time

1 November: No class 8 November

When? Temporal entities and timelines

Assignment

Perform Voyant analysis of HC emails Compare (see next slide for all the available files): Do comparisons in separate Voyant windows

f6-100-raw.txt vs f6-100-normalised.txt to see how text normalisation gives different perspective

• For further comparisons, choose either the raw or the normalised text:
• f6-1000-*.txt vs f7-1000-*.txt to see how the emails are different
• If Voyant or your computer has difficulty with 1000 emails, compare f6-100-*.txt vs f7-

100-*.txt

Download files from Moodle:

Emails Raw Normalised 6000-6099 f6-100-raw.txt f6-100-normalised.txt 7000-7099 f7-100-raw.txt f7-100-normalised.txt 6000-6999 f6-1000-raw.txt f6-1000-normalised.txt 7000-7999 f7-1000-raw.txt f7-1000-normalised.txt

Assignment Work in pairs of two or three Use the tools discussed today to try and find something you find

• interesting. Document your steps and choices and discuss why a finding is
• f interest, and whether you can be certain of this finding.

Hand in the assignment in HTML, include your name and a decent profile photo 500-1000 words, in English

Possible questions you might ask of your corpora

What are these emails about?

• Do we need to further clean the data?
• How are these corpora different?
• Does text normalisation lead to different results?

Grading Do note: the finding itself is not the most important part Email to max.kemman@uni.lu before the start of the next lecture

1pt for free

• 3pts for HTML
• 3pts for documentation of your process
• 3pts for critical reflection on your finding