5 syllables: to, ke, ni, za, tion. Stress on ni.
toh-kuh-niz-AY-shuhn
/ˌtoʊkənɪˈzeɪʃən/
tokenization is pronounced toh-kuh-niz-AY-shuhn (/ˌtoʊkənɪˈzeɪʃən/). It has five syllables (to-ke-ni-za-tion), with the stress on "ni". Tokenization is the process of dividing a stream of text into meaningful units, called tokens, typically words, phrases, or symbols, for further analysis. In computing and NLP, it prepares data for processing by algorithms. The term encompasses methods like word-level, subword, and character-level tokenization, and is fundamental to tasks such as parsing, indexing, and model input generation.
Tokenization is the process of dividing a stream of text into meaningful units, called tokens, typically words, phrases, or symbols, for further analysis. In computing and NLP, it prepares data for processing by algorithms. The term encompasses methods like word-level, subword, and character-level tokenization, and is fundamental to tasks such as parsing, indexing, and model input generation.
- You may flatten the mid syllable or misplace primary stress. Fix by isolating /naɪ/ as the nucleus and ensuring /ˌtoʊ.kə/ precedes the stressed /ˈnaɪ/.- The final -tion can sound like /ʃən/ or /zjən; standard is /-zeɪ.ʃən/. Practice by chunking: /toʊ.kə/ + /ˈnaɪ.zeɪ.ʃən/ and record to hear the contrast.- Another frequent error: substituting /t/ with a dental or alveolar blend in rapid speech; keep light touch on the tongue tip for a clean /t/ and avoid extra aspiration.
"The tokenizer failed to split the sentence correctly at punctuation."
"In natural language processing, tokenization is a crucial preprocessing step."
"We evaluated several tokenization strategies for multilingual data."
You say /ˌtoʊ.kəˈnaɪ·zeɪ·ʃən/ in US and /ˌtəʊ.kən.aɪˈzeɪ.ʃən/ in UK, with primary stress on the third syllable: to-kə-NY-zay-shən. The initial 'to' is unstressed, the 'ka' is quick, the 'ni' forms the peak of stress, and the 'zation' ends with a clear 'zhun' sound. Practice tying the syllables smoothly to avoid a choppy sequence.
Common errors: over-stressing the first syllable (to-), misplacing stress on the 'ny' part (to-KE-ni-za-tion), and mispronouncing the 'z' as a 's' or 'zh' sound. Correct by reinforcing the /ˈnaɪ/ nucleus, keeping /ˌtoʊ.kə/ in the pre-stressed portion, and ensuring the final /ʃən/ sounds like 'zhun' rather than 'shun'. Slow the middle syllables, then speed up while maintaining the same pitch contour.
US: primary stress on the third syllable with a clear /ˈnaɪ/. UK: similar pattern but with less rhoticity on certain vowels and a slightly shorter /ə/ in unstressed positions. AU: vowel qualities may lean toward a flatter /ə/ and a more clipped /ˈnaɪ/ in some speakers. In all, maintain /ˈnaɪ/ as the nucleus, but respond to vowel quality shifts: US tends to brighter vowels, UK softer central vowels, AU more vowel reduction in rapid speech.
The difficulty lies in the multi-syllabic rhythm with a strong /ˌtoʊ.kəˈnaɪ/ core and the final /ˈzeɪ.ʃən/ cluster; the /ˈnaɪ/ nucleus is the energy peak, and the sequence /tə/ or /toʊ/ in unstressed prefixes can blur in fast speech. Also, the 'ti' often merges with 'za-' making it look deceptively simple on paper. Good practice: segment slowly, map mouth positions, then blend.
A unique aspect is the prefix-like unstable length in /ˌtoʊ.kə/ vs /ˌtə.kən/ in some speakers; it's easy to misplace the primary stress onto the wrong syllable in rapid dialogue. Another focus: the 'tion' ending often becomes an /ʃən/ or /zjən/ depending on the speaker; standard form is /-zeɪ.ʃən/. Keep the -za- as a single syllable with a clear /zeɪ/ before the /ʃən/.
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- US: rhotic, clearer /r/ is not a factor here; focus on strong /oʊ/ in to-, and the diphthong in -ny-; UK: flatter /ə/ and slightly shorter vowel duration in unstressed syllables; AU: tendency toward vowel reduction in unstressed syllables and a clipped rhythm. IPA anchors: US /ˌtoʊ.kəˈnaɪˌzeɪ.ʃən/, UK /ˌtəʊ.kən.aɪˈzeɪ.ʃən/, AU /ˌtəʊ.kə.naɪˈzeɪ.ʃən/; maintain /ˈnaɪ/ as the energy peak across accents.
Tokenization derives from the noun token, dating to late Middle English as a small countersign or symbol. In computer science, token is a midthing from 19th-century linguistics and information theory referring to a discrete unit of data or meaning. The verb form tokenize emerged in the mid-20th century alongside the expansion of text processing and programming languages, reflecting the act of converting text into tokens. The combined noun-token + suffix -ization denotes the process of creating tokens, a term popularized with the rise of natural language processing and data mining in the late 1990s and 2000s, when systems began to parse large text corpora and require normalized, discrete units for analysis. First known uses occur in computational linguistics literature and early NLP toolchains, where tokenization was used to describe the segmentation of streams into token units for subsequent tagging, parsing, or indexing.
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Words that rhyme with "tokenization"
-ion sounds
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