A Highly Normalized Relational Schema for Exhaustive Lexical and Linguistic Analysis: Boolean, Enumerated, and Relational Data Modeling

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The computational representation of natural language traditionally suffers from normalization anomalies due to an over-reliance on unstructured string data. In conventional lexicographic systems and digital dictionaries, definitions, phonetic transcriptions, morphological derivations, and grammatical constraints are typically stored as variable-length text strings or loosely structured document models.1 This architectural paradigm severely restricts advanced query capabilities, limits computational efficiency, and creates significant barriers for natural language processing (NLP) algorithms that require deterministic, machine-readable feature matrices.2 To achieve absolute data integrity, interoperability, and high-performance querying, a lexical database schema must be designed under a strict and uncompromising architectural constraint: the only string data type permitted in the entire database is the orthographic representation of the word itself. Every other linguistic property, from morphology and syntax to phonetics and pragmatics, must be modeled exclusively through enumerated types (enums), boolean flags, and foreign key relationships.3

This report outlines the comprehensive architectural blueprint for such a database. By deconstructing lexical units into their atomic, discretized linguistic features, the resulting schema accommodates the immense complexity of global languages, supports advanced Boolean search logic, and establishes a foundational relational graph for typological research, machine translation, and semantic analysis.7

Core Entity Architecture and Morphological Lineage

At the center of this relational database schema lies the primary entity, which encapsulates the fundamental lexical unit. In traditional lexicography, a single database row might represent a "word," but in a normalized computational model, it is necessary to define this entity as a "flow" or a generalized morphosyntactic unit.4 This unit can represent a bound morpheme, an affix, a free morpheme, a fully inflected word, a compound word, or even a multi-word phrase functioning as a single semantic unit.4

To satisfy the foundational constraint that the only string in the database is the word itself, the primary table, designated as LexicalUnit, contains the string field OrthographicForm. All hierarchical, structural, and etymological origins are mapped using self-referencing foreign keys.

Etymological and Morphological Relationships

In morphologically rich languages, new lexical items are frequently generated through derivation and compounding, meaning that a vast majority of the lexicon is structurally dependent on a smaller set of foundational roots.10 To track the structural evolution of a word without utilizing descriptive text, the schema relies on strict relational pointers:

The etymological origin of a word is further modeled using an Etymology junction table. This table maps the current LexicalUnit to ancestor units across different historical languages, employing an enum for the specific type of etymological relationship. Structural parsing of existing etymological databases identifies at least 31 different types of etymological relations, distinguishing between direct genetic inheritance, lateral borrowing, loan translations (calques), and semantic shifts.12 This hierarchical structure preserves relationship topologies, such as the evolution of a term across multiple language boundaries, converting historical linguistics into a queryable mathematical graph.12

Part of Speech (POS) Classification: Universal Dependencies

The fundamental grammatical classification of a word is its Part of Speech (POS). Rather than relying on unstructured text tags or language-specific acronyms, the database schema implements the Universal Dependencies (UD) framework. The UD system is a standardized syntactic classification taxonomy designed to be universally applicable across all human languages, ensuring cross-linguistic consistency.14

The UD taxonomy divides words into three distinct enumerated classes: Open Class, Closed Class, and Other.15 By defining these categories as explicit integer-backed enums, the database ensures referential integrity, eliminates tagging spelling errors, and standardizes downstream syntactic parsing operations.18

Open Class Words

Open class words are categories of lexical items that frequently undergo morphological derivation and are subject to continuous semantic expansion. New words can be freely added to these categories as languages evolve.16

Closed Class and Other Words

Closed class words represent finite, structural components of a language. These are grammatical function words that rarely admit new members and dictate the syntactic architecture of a phrase.16

The strict separation of open and closed classes provides a critical boolean inference parameter for machine learning algorithms. When a parser encounters a word tagged with a closed class enum, it can instantly restrict its predictive branching, as the combinatorial possibilities of closed-class words are mathematically finite.8

Inflectional Morphology: Enumerating Grammatical State

To distinguish the precise grammatical state of a word beyond its core POS tag, the database schema must comprehensively capture inflectional morphology. In the Universal Dependencies framework, these features are explicitly categorized as inflectional features, which represent grammatical properties dictated by the syntactic environment of the sentence.19

Because string descriptors are strictly prohibited, every morphological feature must be modeled as a distinct column within a related MorphologicalFeatures table, utilizing enumerations to define the specific grammatical states.4

Nominal Inflectional Features

Nouns, pronouns, adjectives, and determiners undergo specific inflectional modifications to indicate their role in a phrase. The schema models these properties using the following enums:

Verbal Inflectional Features

Verbs feature the highest degree of morphological complexity, shifting form to indicate time, duration, certainty, and participant relations.

By utilizing distinct, mutually exclusive enumerations rather than arbitrary, sparse boolean matrices (e.g., actively avoiding wide architectures featuring columns like IsPastTense, IsPresentTense, IsFutureTense), the schema future-proofs the database.23 As linguistic researchers append languages with highly exotic tense systems, the central integer-based VerbTense enum can be seamlessly expanded to accommodate new distinct values without requiring an underlying structural schema alteration.24

Lexical Morphology: Inherent Word Properties

While inflectional morphology describes temporary grammatical states, lexical features represent inherent properties of the lexeme itself.19 These are immutable properties that dictate how a word behaves in any context.

Derivational Morphology: The Semantic Calculus of Affixation

Derivational morphology involves the attachment of affixes (prefixes, suffixes, infixes, or circumfixes) to a base root in order to create an entirely new lexical item. Unlike inflectional morphology, derivation frequently alters the syntactic category of the word and applies a distinct semantic shift.27 In a fully normalized, relational schema, prefixes and suffixes must be treated as independent records within the LexicalUnit table itself.4 Their specific attachment to a base word is mediated by a Derivation junction table.

To computationally capture the semantic effect of an affix without resorting to descriptive strings, the database employs a DerivationSemanticCategory enum. This enumeration maps the exact conceptual transformation applied by the affix, acting as a semantic operator.3

Prefix Semantic Operators

Prefixes generally alter the meaning of the word without changing its underlying syntactic POS category.28 The semantic categories modeled via enums provide an exhaustive functional mapping of prefix behaviors 28:

Suffix Semantic Operators

Suffixes are far more syntactically active than prefixes, frequently driving part-of-speech conversion (e.g., transforming a verb into a noun).27 The relational database captures this interaction by observing the POS enum of the BaseWordId, observing the POS enum of the newly derived LexicalUnit, and explicitly logging the transformation via a specific semantic enum.32

By architecturally mapping these derivations through structural relationships, the database permits computational linguists to execute highly complex macro-analytical queries. For instance, determining the statistical frequency of all CausativeVerb formations derived specifically from AbstractState nouns requires only integer-based enum joins across the Derivation table, completely circumventing the processing overhead of wildcard string parsing.27

Phonological and Phonetic Discretization: Matrixing the IPA

A fully comprehensive lexical database must capture the acoustic, auditory, and physiological articulatory properties of words. Because raw string data is prohibited by the schema parameters, traditional International Phonetic Alphabet (IPA) transcriptions (e.g., /hɛˈloʊ/) cannot be stored in a flat text field.36 Instead, phonology must be rigorously discretized into its fundamental physiological features using boolean logic matrices and specific enumerations.38

The schema establishes an independent Phoneme table. Every discrete sound capable of human articulation is defined as a record in this table, mapped entirely by physiological phonetic features.39 A WordPhoneticSequence junction table links the parent LexicalUnit to an ordered list of Phoneme records to represent the linear pronunciation of the word.

Consonant Articulatory Features

Consonants involve a distinct physical constriction or closure of the human vocal tract. They are universally defined by three primary dimensions, which map perfectly to a combination of enums and booleans 41:

Vowel Articulatory Features

Vowels are produced without rigid stricture in the vocal tract. Instead of specific points of contact, they are modeled using geometric coordinates regarding the relative positioning of the tongue body and the lips.40

Suprasegmental and Syllabic Features

Phonology extends beyond individual sounds. To capture syllable boundaries, prosodic stress, and pitch modulation, additional relational flags are applied. A Syllable grouping table tracks the hierarchy of phonemes bound together.

• IsSyllableNucleus Boolean: Distinguishes the resonant core of the syllable (typically a vowel or syllabic consonant) from the onset and coda consonants.45
• StressLevel Enum: Applied explicitly at the syllable level rather than the phoneme level, utilizing values such as Primary, Secondary, and Unstressed.37
• Tone Enum: Essential for the accurate representation of tonal languages (e.g., Mandarin Chinese, Yoruba), with values capturing pitch contours such as High, Low, Rising, Falling, Peaking, and Dipping.43

This highly granular, matrix-driven phonetic modeling allows the database to instantly identify complex phonological patterns through SQL queries. For instance, a query to discover all English words ending in a "voiced velar stop" requires only a search on the ultimate phoneme where PlaceOfArticulation \= Velar, MannerOfArticulation \= Stop, and IsVoiced \= True, completely bypassing the computational overhead of irregular text-based regex matching.49

Lexical Semantics and Ontological Networks (WordNet Integration)

The conceptual meaning of a word is arguably its most fluid and complex attribute. In the absence of lengthy textual definitions, semantic meaning must be captured relationally, drawing heavily on the architectural philosophy pioneered by Princeton WordNet and EuroWordNet.9 The schema abstracts meaning into an independent Synset (synonym set) entity. Individual lexical units are linked to distinct synsets via a WordSense junction table, providing a mathematically sound mechanism for representing polysemy (one word possessing multiple disparate meanings) and synonymy (multiple words sharing one identical meaning).52

Semantic Relations

Synsets do not exist in isolation; they are connected to other synsets via a SemanticRelation table, utilizing a SemanticRelationType enum to build a vast, interconnected ontological graph.51

Lexicographer Categories (Lexnames)

To provide a high-level, immediately queryable semantic domain for every synset without requiring deep graph traversal, the schema utilizes WordNet's 45 Lexicographer Files (lexnames) as an exhaustive LexicographerCategory enum.58 This integer-based enumeration categorizes the logical grouping of concepts across nouns, verbs, adjectives, and adverbs, allowing datasets to be segmented by subject matter instantly.59

Noun Categories (03–28)

Nouns represent the largest block of semantic categorization. The enum values explicitly define the physical or abstract boundaries of the noun 58:

• noun.Tops: Unique beginners; the most abstract nodes of the hierarchy.
• noun.act: Nouns denoting acts, actions, or deeds.
• noun.animal: Nouns denoting living animals and lifeforms.
• noun.artifact: Nouns denoting artificial, man-made objects and structures.
• noun.attribute: Nouns denoting characteristics of people and objects.
• noun.body: Nouns denoting anatomical body parts and systems.
• noun.cognition: Nouns denoting cognitive processes, thoughts, and knowledge.
• noun.communication: Nouns denoting communicative processes, language, and messages.
• noun.event: Nouns denoting natural events, historical occurrences, and phenomena.
• noun.feeling: Nouns denoting emotions, feelings, and subjective sensations.
• noun.food: Nouns denoting comestible foods, drinks, and ingredients.61
• noun.group: Nouns denoting assemblages of people, animals, or objects.
• noun.location: Nouns denoting spatial coordinates, regions, and places.
• noun.motive: Nouns denoting reasons, motivations, and internal drives.
• noun.object: Nouns denoting naturally occurring inanimate objects (e.g., rocks, planets).
• noun.person: Nouns denoting human beings, professions, and demographics.
• noun.phenomenon: Nouns denoting observable physical phenomena.
• noun.plant: Nouns denoting flora and vegetative life.
• noun.possession: Nouns denoting ownership, wealth, and property.
• noun.process: Nouns denoting biological, chemical, or systemic processes.
• noun.quantity: Nouns denoting measurements, metrics, and amounts.
• noun.relation: Nouns denoting relationships between entities.
• noun.shape: Nouns denoting two- and three-dimensional geometry.
• noun.state: Nouns denoting stable states of affairs or conditions.
• noun.substance: Nouns denoting matter, materials, and chemical compounds.
• noun.time: Nouns denoting temporal periods, chronology, and timeframes.

Verb Categories (29–43)

Verbs are categorized by the nature of the action, providing a semantic map of kinetic, emotional, and systemic processes 58:

• verb.body: Verbs of bodily care, grooming, and physical reflexes.
• verb.change: Verbs denoting transformation, size variation, or temperature change.
• verb.cognition: Verbs denoting thinking, judging, learning, and analyzing.
• verb.communication: Verbs denoting telling, asking, writing, and broadcasting.
• verb.competition: Verbs denoting fighting, sports, games, and conflict.
• verb.consumption: Verbs denoting eating, drinking, and using resources.
• verb.contact: Verbs denoting touching, hitting, tying, and physical manipulation.
• verb.creation: Verbs denoting sewing, building, baking, and artistic generation.
• verb.emotion: Verbs denoting feeling, loving, fearing, and reacting.
• verb.motion: Verbs denoting walking, flying, swimming, and spatial transit.
• verb.perception: Verbs denoting seeing, hearing, tasting, and feeling.
• verb.possession: Verbs denoting buying, selling, lending, and owning.
• verb.social: Verbs denoting political and social activities, governance, and events.
• verb.stative: Verbs denoting states of being, having, or static spatial relations.
• verb.weather: Verbs denoting meteorological events (raining, snowing, thundering).

Adjective and Adverb Categories (00–02, 44)

Modifiers are categorized into a smaller subset of domains 58:

• adj.all: A general cluster containing standard descriptive adjectives.
• adj.pert: Relational adjectives, or pertainyms, which directly relate to a noun (e.g., dental relating to tooth).
• adj.ppl: Participial adjectives, derived morphologically from verbs (e.g., broken).
• adv.all: A unified category encompassing all adverbial modifiers.

By mapping every distinct meaning to these highly structured enumerated domains, the database establishes an inherent, language-agnostic conceptual taxonomy.9 This provides an essential foundation for cross-lingual NLP mapping.

Syntactic Valency and Subcategorization Frames

Beyond part-of-speech classification, verbs (and to a lesser extent, certain nouns and adjectives) possess intrinsic syntactic requirements known as valency or subcategorization frames.18 Valency describes the strict number and type of arguments (subjects, direct objects, indirect objects, and prepositional complements) a predicate requires to form a grammatically valid sentence.22

The database schema utilizes two primary mechanisms to capture these syntactic mechanics relationally, completely avoiding natural language rules engines:

Valency Arity

The quantitative argument requirement is represented via the ValencyArity enum. This metric perfectly mirrors mathematical arity, explicitly specifying how many actants are bound to the predicate 22:

Subcategorization Frames

While arity dictates the raw number of required arguments, the SubcategorizationFrame enum dictates their specific syntactic nature and structural configuration.65 Modern dependency parsers and AI language models rely heavily on precise subcategorization frames to resolve structural ambiguities.18 Frame enums rigidly map the exact grammatical environment required by the word 67:

In computational linguistics, mapping verbs to their subcategorization frames using integer-based enums allows syntax parsers to calculate the conditional probability of sentence structures dynamically. This circumvents the massive overhead of parsing text descriptions of grammar rules during runtime, enabling real-time structural disambiguation.69

Pragmatics, Sociolinguistics, and Usage Registers

Words possess pragmatic and sociolinguistic dimensions that rigidly dictate the situational context in which they are appropriately used. These contextual constraints are frequently captured in traditional print dictionaries as italicized usage labels (e.g., slang, archaic, medical).70 In a relational database schema, these labels must be standardized using a strict LinguisticRegister enum.71

Registers identify variations linked to social situation, occupational domain, expectation of formality, and target audience.72 The enumerated values include:

By treating pragmatic usage strictly as categorical metadata, machine translation engines and generative models can align the sociolinguistic register of a source text with the target text, ensuring that a formal document is not erroneously translated into casual slang simply because two words share an underlying semantic Synset.9

Boolean Logic, Query Optimization, and Extensibility

The fundamental architectural decision to completely eliminate descriptive strings in favor of integer-backed enumerations, binary booleans, and self-referencing foreign key relationships yields profound advantages in query optimization and boolean logic execution.6

The Superiority of Enumerations over Wide Boolean Matrices

When designing object-relational databases, it is a common architectural anti-pattern to create wide tables filled with dozens of independent boolean flags (e.g., IsNoun, IsVerb, IsPastTense, IsPresentTense). This practice invariably leads to highly sparse data matrices and introduces logically invalid states (e.g., a single word being erroneously flagged simultaneously as IsPastTense \= True and IsFutureTense \= True).23

By utilizing enums (e.g., VerbTense \= Past), the database schema enforces mutual exclusivity inherently at the data tier. Enums also provide drastically superior horizontal extensibility. If a new language is integrated into the database that features a rare "Remote Past" tense, the integer-based VerbTense enum is simply expanded to accommodate the new value, requiring zero underlying schema alteration or new table columns.24

Native Boolean Logic Processing and Indexing

Because the linguistic metadata consists entirely of discrete mathematical sets (enums and ID relations), the database natively supports highly complex Boolean search algorithms at lightning speeds.6 Using standard logical operators (AND, OR, NOT), the architecture supports highly specific, deeply nested query trees.78

For instance, a computational linguist analyzing morphological trends can execute a boolean query to retrieve "All words that are Nouns (POS \= NOUN) AND denote man-made objects (LexicographerCategory \= noun.artifact) AND NOT derived from a verb (RootWord.POS\!= VERB)." Such a query executes in milliseconds. By utilizing standard B-tree indexing on enum fields or spatial R-tree indexing on phonetic vectors 79, the database achieves a level of high-throughput analysis that is structurally impossible in traditional text-based lexical repositories. This pure mathematical reduction of language guarantees that the database can scale to encompass every word in every human language, preserving absolute structural integrity across the entire linguistic ecosystem.

Works cited

1. Database schema \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/Database\_schema
2. A Gentle Introduction to the Bag-of-Words Model \- MachineLearningMastery.com, accessed May 7, 2026, https://machinelearningmastery.com/gentle-introduction-bag-words-model/
3. Word Classes, Prefixes and Suffixes | nool, accessed May 7, 2026, https://nool.ontariotechu.ca/writing/english-language-resources/grammar/incorrect-prefixes-and-suffixes.php
4. Cheatsheet of Linguistic Properties and their Values? : r/asklinguistics \- Reddit, accessed May 7, 2026, https://www.reddit.com/r/asklinguistics/comments/1309d9w/cheatsheet\_of\_linguistic\_properties\_and\_their/
5. Designing linguistic databases: a primer for linguists \- SciSpace, accessed May 7, 2026, https://scispace.com/pdf/designing-linguistic-databases-a-primer-for-linguists-3t56vzu6cd.pdf
6. Boolean data type \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/Boolean\_data\_type
7. Morphology in multilingual data resources: A brief survey \- Revista do GEL, accessed May 7, 2026, https://revistadogel.emnuvens.com.br/estudos-linguisticos/article/download/4031/2428
8. algorithms for boolean function query properties \- Scott Aaronson, accessed May 7, 2026, https://www.scottaaronson.com/papers/bfpsiam.pdf
9. EuroWordNet Objectives, accessed May 7, 2026, https://archive.illc.uva.nl/EuroWordNet/objectives-ewn.html
10. The Composition and Use of the Universal ... \- UniMorph, accessed May 7, 2026, https://unimorph.github.io/doc/unimorph-schema.pdf
11. Prefixes, Suffixes & Root Words in English | Overview & Examples \- Lesson \- Study.com, accessed May 7, 2026, https://study.com/academy/lesson/spelling-word-patterns-prefixes-suffixes-root-words.html
12. droher/etymology-db \- GitHub, accessed May 7, 2026, https://github.com/droher/etymology-db
13. How to Design a Multilingual Database Structure | A Practical Guide \- DEV Community, accessed May 7, 2026, https://dev.to/adnanbabakan/how-to-design-a-multilingual-database-structure-a-practical-guide-35nf
14. Morphology: General Principles \- Universal Dependencies, accessed May 7, 2026, https://universaldependencies.org/u/overview/morphology.html
15. Universal POS tags \- Universal Dependencies, accessed May 7, 2026, https://universaldependencies.org/u/pos/index.html
16. Universal POS tags, accessed May 7, 2026, https://universaldependencies.org/u/pos/all.html
17. Universal POS tags, accessed May 7, 2026, https://universaldependencies.org/u/pos/
18. Schema of the database of subcategorization frames. \- ResearchGate, accessed May 7, 2026, https://www.researchgate.net/figure/Schema-of-the-database-of-subcategorization-frames\_fig1\_42803652
19. Universal features, accessed May 7, 2026, https://universaldependencies.org/u/feat/all.html
20. Grammatical Features Inventory \- Surrey Morphology Group, accessed May 7, 2026, https://www.smg.surrey.ac.uk/features/
21. Valency Frames of Czech Verbs in VALLEX 1.0 \- ACL Anthology, accessed May 7, 2026, https://aclanthology.org/W04-2711.pdf
22. Valency (linguistics) \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/Valency\_(linguistics)
23. Why is it bad to use boolean flags in databases? And what should be used instead?, accessed May 7, 2026, https://stackoverflow.com/questions/3042538/why-is-it-bad-to-use-boolean-flags-in-databases-and-what-should-be-used-instead
24. Don't Use Boolean Arguments, Use Enums : r/programming \- Reddit, accessed May 7, 2026, https://www.reddit.com/r/programming/comments/g9sxyj/dont\_use\_boolean\_arguments\_use\_enums/
25. Two member enumeration vs. boolean value \- Stack Overflow, accessed May 7, 2026, https://stackoverflow.com/questions/1686591/two-member-enumeration-vs-boolean-value
26. SQL: enum vs reference table when values are referenced in the code, accessed May 7, 2026, https://softwareengineering.stackexchange.com/questions/430280/sql-enum-vs-reference-table-when-values-are-referenced-in-the-code
27. Derivational Affixes and Their Functions \- Zenodo, accessed May 7, 2026, https://zenodo.org/records/15010013/files/36-39+Derivational+Affixes+and+Their+Functions.pdf?download=1
28. Most Common Prefixes and Suffixes \- Scholastic, accessed May 7, 2026, https://www.scholastic.com/content/dam/teachers/lesson-plans/migrated-files-in-body/prefixes\_suffixes.pdf
29. Appendix:English prefixes \- Wiktionary, the free dictionary, accessed May 7, 2026, https://en.wiktionary.org/wiki/Appendix:English\_prefixes
30. 5.7 Valency patterns, accessed May 7, 2026, https://www.torosceviri.info/wp-content/uploads/2020/01/BCL-valency-patterns.pdf
31. Derivational Suffix List \- Lexical Tools \- NIH, accessed May 7, 2026, https://lhncbc.nlm.nih.gov/LSG/Projects/lvg/current/docs/designDoc/UDF/derivations/suffixList.html
32. Syntactic category information and the semantics of derivational morphological rules, accessed May 7, 2026, https://www.anglistik3.hhu.de/fileadmin/redaktion/Fakultaeten/Philosophische\_Fakultaet/Anglistik\_und\_Amerikanistik/Ang3\_Linguistics/Dateien/Detailseiten/Plag/Plag2004\_Syntactic\_category\_information\_and\_the\_semantics\_of\_derivational\_morphological\_rules.pdf
33. Comprehensive Prefixes & Suffixes Guide | PDF \- Scribd, accessed May 7, 2026, https://www.scribd.com/document/461246832/complete-list-of-prefixes
34. Master List of Morphemes Suffixes, Prefixes, Roots Suffix Meaning \*Syntax Exemplars, accessed May 7, 2026, https://www.fldoe.org/core/fileparse.php/16294/urlt/morphemeML.pdf
35. Meanings of Common Derivational Suffixes \- Spelling \- Sight Words, accessed May 7, 2026, https://www.sightwordsgame.com/spelling/suffix/meanings-common-derivational-suffixes/
36. Naming conventions of the International Phonetic Alphabet \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/Naming\_conventions\_of\_the\_International\_Phonetic\_Alphabet
37. International Phoneme Representation (SAPI 5.3) | Microsoft Learn, accessed May 7, 2026, https://learn.microsoft.com/en-us/previous-versions/windows/desktop/ms720567(v=vs.85)
38. PanPhon, phonetic features database for many IPA. : r/conlangs \- Reddit, accessed May 7, 2026, https://www.reddit.com/r/conlangs/comments/aehq8o/panphon\_phonetic\_features\_database\_for\_many\_ipa/
39. 4.3 Phonetic Segments and Features – Essential of Linguistics \- Maricopa Open Digital Press, accessed May 7, 2026, https://open.maricopa.edu/essentialsoflinguistics/chapter/4-4-phonetic-segments-and-features/
40. Sound Features \- LanGo Institute, accessed May 7, 2026, https://langoinstitute.com/blog/2020/9/16/sound-features
41. More IPA For American Consonants: Place, Manner, & Voicing | International Phonetic Alphabet, accessed May 7, 2026, https://sandiegovoiceandaccent.com/american-english-consonants/place-manner-and-voicing-of-the-american-english-consonants
42. Place Manner Voice Chart | The Speech Guide, accessed May 7, 2026, https://thespeechguide.com/place-manner-voice-chart/
43. Phonological Features Chart, accessed May 7, 2026, http://www.artoflanguageinvention.com/papers/features.pdf
44. Describing English vowels, accessed May 7, 2026, https://home.cc.umanitoba.ca/\~krussll/phonetics/articulation/describing-vowels.html
45. Vowel \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/Vowel
46. Describing Vowels – ENGL 6360 – Descriptive Linguistics for Teachers, accessed May 7, 2026, https://pressbooks.utrgv.edu/engl6360/chapter/describing-vowels/
47. English phonology \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/English\_phonology
48. Table of linguistic features, accessed May 7, 2026, https://www.unige.ch/innovations-pedagogiques/application/files/9716/9392/3702/Genoveva\_Puskas\_Table\_of\_languages\_of\_features.pdf
49. insert IPA into MySQL \- Stack Overflow, accessed May 7, 2026, https://stackoverflow.com/questions/32759604/insert-ipa-into-mysql
50. Uses of Enum Class ca.phon.ipa.PhoneDimension, accessed May 7, 2026, https://www.phon.ca/apidocs/ca/phon/ipa/class-use/PhoneDimension.html
51. wngloss(7WN) | WordNet, accessed May 7, 2026, https://wordnet.princeton.edu/documentation/wngloss7wn
52. WordNet, accessed May 7, 2026, https://wordnet.princeton.edu/
53. WordNet Full Schema \- W3C, accessed May 7, 2026, https://www.w3.org/2006/03/wn/wn20/schemas/wnfull.rdfs
54. WordNet \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/WordNet
55. UniRoma2 \- A Bottom-up Comparative Study of EuroWordNet and WordNet 3.0 Lexical and Semantic Relations, accessed May 7, 2026, http://art.uniroma2.it/publications/docs/2008\_LREC2008\_A%20Bottom-up%20Comparative%20Study%20of%20EuroWordNet%20and%20WordNet%203.0%20Lexical%20and%20Semantic%20Relations.pdf
56. EuroWordNet General Document1 \- Global WordNet Association, accessed May 7, 2026, https://globalwordnet.github.io/gwadoc/pdf/EWN\_general.pdf
57. WordNet, EuroWordNet and Global WordNet \- SciSpace, accessed May 7, 2026, https://scispace.com/pdf/wordnet-eurowordnet-and-global-wordnet-i2jr4h17cy.pdf
58. lexnames(5WN) \- WordNet \- Princeton University, accessed May 7, 2026, https://wordnet.princeton.edu/documentation/lexnames5wn
59. lexnames \- List of WordNet lexicographer file names and numbers \- Ubuntu Manpage, accessed May 7, 2026, https://manpages.ubuntu.com/manpages/focal/en/man5/lexnames.5WN.html
60. wninput(5WN) | WordNet \- Princeton University, accessed May 7, 2026, https://wordnet.princeton.edu/documentation/wninput5wn
61. WordNet Get Wordlist Of Lexicographer Files \- Stack Overflow, accessed May 7, 2026, https://stackoverflow.com/questions/56724056/wordnet-get-wordlist-of-lexicographer-files
62. WordNet File Formats LEXNAMES ( 5WN ) List of WordNet lexicographer file names and numbers During WordNet development synsets \- Directory Listing, accessed May 7, 2026, https://courses.cs.tau.ac.il/0368-4341/shared/Resources/WordNet-3.0/doc/pdf/lexnames.5.pdf
63. (PDF) Situation types, valency frames and operations \- ResearchGate, accessed May 7, 2026, https://www.researchgate.net/publication/325494097\_Situation\_types\_valency\_frames\_and\_operations
64. Verb valency classes in Evenki in the comparative perspective, accessed May 7, 2026, https://www.eva.mpg.de/lingua/conference/2013\_valency\_classes/pdf/IgorSupplement.pdf
65. SubCategorization Frame of a verb \- Linguistics Stack Exchange, accessed May 7, 2026, https://linguistics.stackexchange.com/questions/38881/subcategorization-frame-of-a-verb
66. An Empirical Architecture for Verb Subcategorization Frame \- a Lexicon for a Real-world Scale Japanese-English Interlingual MT \- ACL Anthology, accessed May 7, 2026, https://aclanthology.org/C96-2108.pdf
67. 1 Lexicon and subcategorization frames, accessed May 7, 2026, http://ling-blogs.bu.edu/lx321f19/assets/pdf/lx321f19-05.pdf
68. AUTOMATIC ACQUISITION OF A LARGE SUBCATEGORIZATION DICTIONARY FROM CORPORA \- Stanford NLP Group, accessed May 7, 2026, https://nlp.stanford.edu/\~manning/papers/subcats.pdf
69. Verb Sense and Verb Subcategorization Probabilities \- Stanford University, accessed May 7, 2026, https://web.stanford.edu/\~jurafsky/CUNY98\_bookchapter.pdf
70. The Codification of Usage by Labels \- ResearchGate, accessed May 7, 2026, https://www.researchgate.net/publication/46640087\_The\_Codification\_of\_Usage\_by\_Labels
71. Register (sociolinguistics) \- Wikipedia, accessed May 7, 2026, https://en.wikipedia.org/wiki/Register\_(sociolinguistics)
72. What is a Linguistic Register & Language Register? A quick way to improve your English, accessed May 7, 2026, https://textinspector.com/linguistic-register-and-language-register/
73. Linguistic Register., accessed May 7, 2026, https://ccat.sas.upenn.edu/\~haroldfs/messeas/regrep/node2.html
74. Boolean vs Keyword/Lexical search vs Semantic — keeping things straight | by Aaron Tay, accessed May 7, 2026, https://aarontay.medium.com/boolean-vs-keyword-lexical-search-vs-semantic-keeping-things-straight-95eb503b48f5
75. Boolean Logic Quick Reference Guide \- Help Center \- AlphaSense, accessed May 7, 2026, https://help.alpha-sense.com/hc/en-us/articles/41630689127571-Boolean-Logic-Quick-Reference-Guide
76. Search Operators \- Web of Science, accessed May 7, 2026, https://webofscience.zendesk.com/hc/en-us/articles/20016122409105-Search-Operators
77. Boolean Operators | Quick Guide, Examples & Tips \- Scribbr, accessed May 7, 2026, https://www.scribbr.com/working-with-sources/boolean-operators/
78. Searching with Boolean Operators \- EBSCO Connect, accessed May 7, 2026, https://connect.ebsco.com/s/article/Searching-with-Boolean-Operators
79. Database Terminology \- Top 150 Database Terms \- Raima \- RaimaDB, accessed May 7, 2026, https://raima.com/database-terminology/