How to Study

Read the textbook chapter alongside the video lesson or podcast. Use the chapter’s explanations, worked examples, Practice and Apply tasks, Short Quiz, and Answers and Explanations to check your understanding.

The short overviews and plain-English topic explanations below are simplified study support. Return to the textbook for full definitions, evidence, examples, and limitations.

Explore the 15 Chapters

Chapter titles and topic headings retain the textbook’s wording. Overviews and plain-English explanations are separate simplified study support.

Chapter 1: Foundations of Corpus Linguistics and Language Data

Textbook: pp. 15–27

Overview: simplified study support

Explore how a carefully selected collection of language can support research. Compare corpus evidence with other kinds of evidence and learn why counts and examples need context.

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Five core topics

1.1 Corpora, Corpus Linguistics, and Language Data
In plain English: Use a documented collection of language to examine patterns through counting and close reading.

1.2 From Early Collections to Digital Corpora
In plain English: Trace how language collections became searchable while remembering that larger collections are not always better for a question.

1.3 Corpus Evidence, Intuition, Experiments, and Other Evidence
In plain English: Choose evidence that fits the question, since language records alone cannot show everything about understanding or intention.

1.4 Corpus-Based and Corpus-Driven Approaches
In plain English: Compare starting with an existing idea with discovering patterns in data, and explain how the study moves between them.

1.5 What Corpus Evidence Can and Cannot Show
In plain English: Limit conclusions to what the collection and search can support, including when a form is absent.

Chapter 2: Corpus Design, Research Questions, and Representativeness

Textbook: pp. 28–40

Overview: simplified study support

A useful corpus begins with a clear question and a plan for selecting texts. Examine who and what the collection covers before deciding how far its findings can apply.

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Five core topics

2.1 From Language Questions to Corpus Research Questions
In plain English: Turn a broad language interest into a specific question with observable features and clear counting rules.

2.2 Purpose, Population, and Representativeness
In plain English: Explain which texts or language events the study concerns and how well the collection covers them.

2.3 Sampling Frames, Text Selection, and Text Length
In plain English: State where texts can be selected, how they are chosen, and how much of each text is included.

2.4 Balance and Corpus Composition
In plain English: Describe what is balanced across groups and check other differences that could affect the comparison.

2.5 Corpus Size, Coverage, and Real Design Cases
In plain English: Choose a size and range of material that fit the question, rather than assuming more words solve design problems.

Chapter 3: Types of Corpora and Corpus Resources

Textbook: pp. 41–53

Overview: simplified study support

Different corpus types preserve different kinds of language evidence. Compare their purposes, contents, and access conditions to choose a resource that fits your study.

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Five core topics

3.1 General, Reference, and Specialized Corpora
In plain English: Distinguish a broad collection, a focused collection, and a collection used as a comparison baseline.

3.2 Written, Spoken, Multimodal, and Signed-Language Corpora
In plain English: Check which features of writing, speech, images, or signed interaction a collection preserves or leaves out.

3.3 Learner, Multilingual, Parallel, and Comparable Corpora
In plain English: Compare learner data, multiple-language collections, aligned translations, and independently produced texts selected on similar grounds.

3.4 Diachronic, Monitor, Web, and Social-Media Corpora
In plain English: Examine dates, collection procedures, duplication, and coverage when studying changing or online language.

3.5 Finding and Accessing Corpora: Interfaces, Downloads, and Licenses
In plain English: Read official documentation and permissions before deciding how a corpus can be searched, downloaded, or reused.

Chapter 4: Collecting and Preparing Language Data

Textbook: pp. 54–66

Overview: simplified study support

Raw files need careful preparation before they become usable corpus evidence. Follow the path from collection and transcription to documented working files without silently changing the original language.

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Five core topics

4.1 Sources of Written, Web, and Social-Media Data
In plain English: Define what belongs in each text, record its source, and check errors introduced when material is collected or converted.

4.2 Recording and Transcribing Spoken and Multimodal Language
In plain English: Choose a recording and transcription method that preserves the features needed for the question.

4.3 Cleaning, Preprocessing, and File Formats
In plain English: Record cleaning and word-counting rules so that changes to files remain clear and repeatable.

4.4 Character Encoding and Multilingual Scripts
In plain English: Check that text characters are stored and read correctly, and document changes to scripts or spelling features.

4.5 Metadata, Documentation, and Data Provenance
In plain English: Connect each text with consistent information about its source, conditions, permissions, and preparation.

Chapter 5: Annotating Corpus Data

Textbook: pp. 67–79

Overview: simplified study support

Adding labels to corpus texts makes new kinds of searches possible. Learn what different annotation layers represent and why both human and automatic labels need checking.

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Five core topics

5.1 Markup and Annotation: Adding Information to Texts
In plain English: Distinguish text structure, information about a text, and labels describing the language within it.

5.2 Part-of-Speech Tagging and Tagsets
In plain English: Use a defined set of grammatical labels and consider a word’s role in its context.

5.3 Lemmatization and Morphological Annotation
In plain English: Group related grammatical forms under a lemma without confusing that group with a root or wider word family.

5.4 Syntactic, Semantic, and Discourse Annotation
In plain English: Examine labels for structure, meaning, and discourse while allowing uncertain cases to remain uncertain.

5.5 Automatic and Manual Annotation: Accuracy and Consistency
In plain English: Use clear guidelines and independent checks, since agreement between annotators does not always mean accuracy.

Chapter 6: Searching Corpora and Reading Concordances

Textbook: pp. 80–92

Overview: simplified study support

A search result is a starting point for analysis. Learn how query rules affect what is found and use wider context to interpret examples carefully.

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Five core topics

6.1 Concordancers, Online Interfaces, and Corpus Search Tools
In plain English: Choose a search tool whose data, functions, and access conditions match the research question.

6.2 Searching for Words, Phrases, Wildcards, and Patterns
In plain English: Check how the tool handles exact words, phrases, wildcards, and character patterns before interpreting results.

6.3 Searching by Lemma, Part of Speech, and Structured Queries
In plain English: Use grammatical and lemma searches only with the relevant annotations and the tool’s documented query rules.

6.4 Key Word in Context: Sorting, Filtering, and Sampling
In plain English: Organize examples around the search match while recording how results were sorted, filtered, or sampled.

6.5 Reading Concordance Evidence in Wider Context
In plain English: Read beyond the displayed line and check both irrelevant matches and relevant cases the search may have missed.

Chapter 7: Frequency and Basic Quantitative Reasoning

Textbook: pp. 93–105

Overview: simplified study support

A larger count does not always mean that a feature is more common in comparable language. Examine counting units, rates, text distribution, and uncertainty before drawing a conclusion.

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Five core topics

7.1 Tokens, Types, Lemmas, and Frequency Lists
In plain English: State whether a list counts word occurrences, distinct forms, lemmas, or another clearly defined unit.

7.2 Raw and Normalized Frequency
In plain English: Compare rates using the same counting rules and report the original counts and amounts of text as well.

7.3 Frequency Profiles and Lexical Diversity
In plain English: Interpret vocabulary variety with attention to text length, task, and the chosen measure.

7.4 Range and Dispersion Across Texts
In plain English: Check how many texts contain a feature and whether its occurrences are spread out or concentrated.

7.5 Comparing Frequencies: Significance and Effect Size in Plain Terms
In plain English: Consider the size of a difference, uncertainty, and study design rather than treating statistical significance as importance or proof of a cause.

Chapter 8: Collocation and Word Association

Textbook: pp. 106–118

Overview: simplified study support

Words can form recurring associations with other words and grammatical patterns. Learn how search windows, measures, and context shape those findings and their visual displays.

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Five core topics

8.1 Co-Occurrence, Collocation Windows, and Candidate Collocates
In plain English: Define which nearby positions or relationships count when looking for words that occur together.

8.2 Association Measures: Mutual Information, t-Score, and Log Dice
In plain English: Check what each association measure emphasizes and inspect counts before trusting a ranked result.

8.3 Colligation: Words and Grammatical Patterns
In plain English: Look for recurring grammatical environments as well as particular neighboring words.

8.4 Semantic Preference and Semantic Prosody
In plain English: Use context to examine meaning groups and evaluative tendencies without assigning a fixed attitude to every use of a word.

8.5 Visualizing and Comparing Collocations
In plain English: Read a graph’s legend and underlying counts, and compare results using suitable shared settings.

Chapter 9: Keywords, N-Grams, and Formulaic Language

Textbook: pp. 119–131

Overview: simplified study support

Corpus comparisons can highlight distinctive words, while repeated sequences reveal possible phrase patterns. Interpret these results through the chosen baseline and the contexts in which the language occurs.

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Five core topics

9.1 Target Corpora, Reference Corpora, and Keywords
In plain English: Explain why a comparison collection is suitable, since it determines what counts as distinctive.

9.2 Keyness Measures and Critical Interpretation
In plain English: Read keyness scores with their counts, direction, and assumptions rather than treating them as measures of importance.

9.3 N-Grams, Clusters, and Lexical Bundles
In plain English: Identify repeated adjacent word sequences using stated counting and boundary rules.

9.4 Phrase Frames, Formulaic Language, and Phraseology
In plain English: Examine fixed and variable phrase patterns without assuming that repetition proves how an expression is stored in the mind.

9.5 Keywords and Collocation in Corpus-Assisted Discourse Analysis
In plain English: Combine word lists, associations, and contextual reading to check discourse interpretations and who expresses a view.

Chapter 10: Corpora, Vocabulary, Grammar, and Lexico-Grammar

Textbook: pp. 132–144

Overview: simplified study support

Corpus evidence shows how vocabulary and grammar work together in particular settings. Use it to study meanings, structures, and learning priorities without confusing frequent use with a universal rule.

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Five core topics

10.1 Word Frequency, Vocabulary Levels, and Learning Priorities
In plain English: Use relevant frequency evidence alongside learner goals, while distinguishing vocabulary coverage from understanding.

10.2 Word Meaning in Use and Corpus-Based Dictionaries
In plain English: Examine examples in context to identify meanings, grammatical patterns, and useful dictionary information.

10.3 Grammar in Use: Frequency and Distribution of Structures
In plain English: Define which structures and possible choices are counted before comparing their use.

10.4 Lexico-Grammar and Patterning
In plain English: Study how particular words connect with recurring grammatical patterns and meanings.

10.5 Spoken and Written Grammar Compared
In plain English: Compare specific spoken and written situations rather than assuming that every text in one mode follows the same pattern.

Chapter 11: Register, Genre, Variation, and Language Change

Textbook: pp. 145–158

Overview: simplified study support

Language patterns differ across situations, communities, and periods. Design comparisons that describe these conditions clearly and avoid explaining every difference through one broad label.

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Five core topics

11.1 Register Analysis: Situation, Linguistic Features, and Function
In plain English: Connect the situation with observed language features and inspect how those features function in context.

11.2 Genre and Recurring Communicative Practices
In plain English: Examine recurring text purposes and stages while recognizing that conventions can vary across communities.

11.3 Online Registers and Digital Language
In plain English: Describe the particular online activity, audience, and interaction instead of treating all internet language as one type.

11.4 Regional, Social, and World Englishes Variation
In plain English: Document variation without ranking communities, and check whom a regional corpus actually represents.

11.5 Diachronic Corpora and Language Change
In plain English: Compare periods using suitable rates and text types so that changing corpus contents are not mistaken for language change.

Chapter 12: Learner Corpora and Language Development

Textbook: pp. 159–173

Overview: simplified study support

Learner corpora can show what additional-language users do in specified tasks and settings. Study their achievements and difficulties while considering background, task conditions, and the limits of group comparisons.

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Five core topics

12.1 Learner Corpus Design and Learner Metadata
In plain English: Record relevant learner and task information while collecting only personal data justified by the study.

12.2 Major Learner Corpora: ICLE, ICNALE, and Other Resources
In plain English: Check the edition, components, tasks, participants, and access conditions of a learner-corpus resource.

12.3 Error Annotation and Learner-Feature Analysis
In plain English: Use explicit criteria for error labels and study successful language features as well as difficulties.

12.4 Comparing Learner and Reference Corpora
In plain English: Choose a suitable reference group and do not assume a frequency difference shows inappropriate language use.

12.5 First-Language Influence, Proficiency, and Longitudinal Development
In plain English: Consider several possible explanations for group differences and distinguish following learners over time from comparing different groups.

Chapter 13: Corpus-Informed Language Teaching and Data-Driven Learning

Textbook: pp. 174–188

Overview: simplified study support

Corpus evidence can inform teaching resources and help students investigate language themselves. Design focused activities with guidance and evaluate what students learn rather than relying on the presence of a tool.

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Five core topics

13.1 Indirect Uses: Dictionaries, Materials, Syllabuses, and Examples
In plain English: Use corpus evidence to inform resources while considering learner goals, difficulty, and teaching time.

13.2 Direct Uses: Data-Driven Learning
In plain English: Help learners notice and test patterns in examples, whether they use a computer or prepared paper materials.

13.3 Designing Data-Driven Learning Activities
In plain English: Connect a clear learning goal with noticing, explanation, testing, application, and feedback.

13.4 Evidence, Benefits, and Limitations of Data-Driven Learning
In plain English: Evaluate benefits and difficulties in the actual learning setting instead of assuming one approach works everywhere.

13.5 Corpora and Generative AI in Language Learning
In plain English: Distinguish generated language from retrieved corpus examples and check AI claims against suitable evidence.

Chapter 14: Ethics and Responsible Use of Language Data

Textbook: pp. 189–203

Overview: simplified study support

Responsible corpus work protects the people behind language data and explains how evidence was obtained. Consider consent, privacy, reuse conditions, and transparent methods throughout a project.

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Five core topics

14.1 Consent, Privacy, Anonymization, and Participant Protection
In plain English: Explain the project clearly, respect voluntary participation, and collect only justified information with suitable protection.

14.2 Copyright, Licensing, Access, and Text Reuse
In plain English: Distinguish being able to read material from having permission to collect, share, or adapt it.

14.3 Public and Private Online Data
In plain English: Consider people’s expectations and possible harm as well as whether an online message can be viewed.

14.4 Data Sharing, Documentation, and Reproducibility
In plain English: Document data and methods so an analysis can be checked without making sensitive or restricted material public.

14.5 AI-Generated Language and Corpus Evidence: Provenance, Accuracy, and Transparency
In plain English: Label generated material and preserve its origin rather than silently mixing it with observed human language.

Chapter 15: Building a Corpus and Conducting a Small Corpus Study

Textbook: pp. 204–217

Overview: simplified study support

Bring the earlier methods together in a complete small corpus project. Use the chapter’s eight constructed teaching texts to practise analysis while keeping conclusions limited to that dataset.

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Five core topics

15.1 From Research Question to Corpus and Analysis Plan
In plain English: Connect a narrow question with the defined dataset, comparison, and counting rules before analysis begins.

15.2 Building and Documenting a Small Specialized Corpus
In plain English: Build the documented eight-text collection and keep text identifiers separate from the words being counted.

15.3 Choosing Tools, Queries, Measures, and Comparison Procedures
In plain English: Choose only the tools and measures needed, and check that manual and computer counts follow the same rules.

15.4 Interpreting and Visualizing Findings with Caution
In plain English: Present counts and rates clearly while showing the dataset’s small size and constructed origin.

15.5 Reporting Methods, Results, Limitations, and Reproducibility
In plain English: Report the question, data, methods, findings, and limitations so another reader can understand and check the work.

About the Textbook

Foundations of Corpus Linguistics and Language Data: For EFL University Students
Prof. Munassir Alhamami · 2026 · First Edition, Version 1

Suggested citation

Alhamami, M. (2026). Foundations of corpus linguistics and language data: For EFL university students (1st ed., Version 1). Zenodo. https://doi.org/10.5281/zenodo.22923749

License

Original textbook content is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), except where otherwise indicated. Material credited to other sources remains subject to its stated terms.

AI development assistance

The textbook discloses development assistance from ChatGPT, Claude, Gemini, and NotebookLM.

Chapter media disclosure

The chapter videos and podcasts were generated with Google NotebookLM using the textbook as their source. Narration and conversation voices are AI-generated, not recordings of the author. The original NotebookLM videos and audio are preserved unchanged; podcast audio is presented with a static textbook-cover image.

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