Generating over 2000 flashcards from a DIY corpus of TOEFL material

Download the CSV of all 2,313 terms (inc. Japanese definitions) or access the full list on Quizlet.

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Step 1: Assemble a corpus of TOEFL past papers

TOEFLFor my corpus, I used material from both the older CBT (Computer Based Test) and the current iBT (Internet Based Test). I found most of the materials online for free. Some were already in plain text format, but most were PDFs and required Optical Character Recognition (OCR) to convert to plain text. I used ABBYY’s FineReader Pro for Mac, but there are plenty of other options out there too. Some files were Microsoft Word format (.doc/.docx), and MacOS X’s batch conversion utility came in hand for these. I included model answers, listening transcripts, reading passages and multiple choice questions (prompts, distractors and answers). I tried to exclude explanations, advice and instructions from the authors and/or publishers.

Ultimately, I ended up with corpus just shy of a million words (959,124 to be precise). In general, bigger is better when it comes to corpus research. The TOEIC Service List (TSL) utilizes a corpus of about 1.5 million words, so my TOEFL corpus seems roughly comparable to this.

Step 2: Count the number of occurrences of each word

I used some custom PHP code to process my corpus data (although Python is probably more suited for corpus analysis). I lemmatized each token where possible using Yasumasa Someya’s list of lemmas. I then cross referenced each lemma occurrence with the NGSL, NAWL and TSL. Finally, I exported to a CSV, and ended up with 13,287 rows of data.

Step 3: Curate the final list

For my final list I removed any words which also appear on the NGSL, any contractions (e.g. “Don’t”,”I’m”,”that’s”), any numbers written in word form (e.g. “two”,”million”), any vocalizations (e.g. “uh”,”oh”), any ordinals (e.g. “first”,”second”,”third”), any proper nouns (“James”, “Elizabeth”, “America”, “San Francisco”, “New York”), and any words with fewer than 5 occurrences in the corpus. Next, I ran the list through a spell checker, and excluded any unrecognized words. I also excluded any non-lexical words, to leave a list consisting only of nouns, verbs, adjectives and adverbs.

Step 4. Generate flashcards

I now had a list of 2313 terms, made up of 523 adjectives, 123 adverbs, 1366 nouns, and 301 verbs. I used Text to Flash to generate Japanese definitions for each word, then uploaded the words to Quizlet, separated into part-of-speech and ordered alphabetically.

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