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zero_shot
|
What is the purpose of tokenizing text into sentences and words?
|
Okay, let's delve into the process of tokenizing text into sentences and words.
### 1. Introductory Overview
Tokenization is a fundamental step in natural language processing (NLP) that involves breaking down text into smaller units, called tokens. These tokens can be words, sub-words, or even characters. Sentence tokenization specifically divides text into individual sentences, while word tokenization breaks down each sentence into its constituent words. This process is crucial because it transforms raw text into a structured format that machine learning models can understand and process effectively. Without tokenization, it would be exceedingly difficult for computers to analyze and make sense of human language.
### 2. Technical Overview
The primary goal of tokenization is to create a set of discrete units from the continuous stream of text. Here's a breakdown of the technical aspects:
* **Sentence Tokenization:** This involves identifying sentence boundaries. Algorithms often rely on punctuation marks (periods, question marks, exclamation points) and sometimes consider contextual clues, such as capitalization and sentence structure, to accurately split the text. Some approaches use rule-based methods, while others use machine learning models trained to identify sentence boundaries (Kiss & Strunk, 2006).
* **Word Tokenization:** This typically involves splitting sentences into individual words. This process may also involve removing punctuation, converting all words to lowercase, and handling contractions and special characters. More advanced tokenizers may use dictionaries or language-specific rules to handle complex cases, such as compound words or idiomatic expressions. In some cases, sub-word tokenization techniques like Byte Pair Encoding (BPE) or WordPiece are employed to handle out-of-vocabulary words and improve model performance (Sennrich et al., 2015).
### 3. Example-Based Expansion
Tokenization is a prerequisite for many NLP tasks. Consider text summarization (as noted in Context Chunk 2), where accurate sentence tokenization is necessary to identify the individual sentences that will be summarized.
In the context of image captioning (as mentioned in Context Chunk 1), the process of tokenizing text descriptions into words is used in sequence-to-sequence models. These models map an image to a sequence of words.
Furthermore, in sentiment analysis and text classification, tokenization is one of the first steps. As seen in Context Chunk 0, tokenized words are converted into vector representations that CNNs then use to categorize the text.
### 4. Broader Exploration
Tokenization is not a standalone process; it's a crucial step that paves the way for more advanced NLP tasks. Here are some related areas for further exploration:
* **Text Preprocessing:** Tokenization is often combined with other preprocessing steps, such as stemming and lemmatization, which reduce words to their root forms.
* **Word Embeddings:** After tokenization, words are typically converted into numerical representations, such as word embeddings (e.g., Word2Vec, GloVe, FastText). These embeddings capture semantic relationships between words and are used as input for various machine learning models.
* **Advanced Tokenization Techniques:** Explore sub-word tokenization methods like BPE or WordPiece that are effective in handling large vocabularies and rare words.
* **Applications:** Consider how tokenization is used in machine translation, question answering, and chatbot development.
|
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6834,
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[
"### **1 Introduction** \nOur focus in this work is on the practically important task of sentence categorization. Convolutional Neural Networks (CNNs) have recently\nbeen shown to perform quite well for this task\n(Kim, 2014; Kalchbrenner et al., 2014; Wang et\nal., 2015; Goldberg, 2015). Such models capitalize on distributed representations of words by\nfirst converting the tokens comprising each instance into a vector, forming a matrix to be used",
"our goal is to *translate* an image into a description. This point of view has also been used by [28]\nand allows us to make use of existing ideas in the machine translation literature. Furthermore, there\nis a natural correspondence between the concept of scoring functions (how well does a caption and\nimage match) and alignments (which parts of a description correspond to which parts of an image)\nthat can naturally be exploited for generating descriptions. \n3 \n-----",
"### **1 Introduction** \n#### Text summarization aims at generating accurate and concise summaries from input document(s). In contrast to extractive summarization which merely copies informative fragments from the input, ab- stractive summarization may generate novel words. A good abstractive summary covers principal infor- mation in the input and is linguistically fluent. In abstractive summarization, sequence-to- sequence (Sutskever et al., 2014) has become a\n###### ∗ Equal contribution. † Work completed during internship at Google Research, Brain Team."
] |
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Definition and Context
Tokenization is the process of breaking text into smaller units called tokens. Typically, this involves segmenting raw text into sentences and further into words (or subwords) . In essence, tokenization maps a stream of characters into meaningful elements (words, punctuation, etc.), discarding whitespace and some formatting. This step is foundational in NLP, as it converts unstructured text into a structured form that algorithms can work with . Each token (e.g., a word) can then be treated as a feature for language models or text classification tasks.
Explanation
Most NLP pipelines begin with tokenization because machine learning models cannot directly interpret raw strings of text. By tokenizing, we delineate sentence boundaries and isolate words, making it easier to analyze syntax and meaning. For example, consider the text:
“Hello world! It’s 2025.”
Sentence tokenization would split this into two sentences: [“Hello world!”, “It’s 2025.”]. Word tokenization on the first sentence yields tokens: [“Hello”, “world”, “!”]. On the second, it yields [“It”, “’s”, “2025”, “.”]. These tokens feed into subsequent processing (parsing, vectorization, etc.). Without tokenization, a model would have to treat the entire string "Hello world! It's 2025." as one sequence, which is far less effective.
Tokenization also handles language-specific needs. For instance, languages like Chinese or Thai do not use spaces to separate words, so explicit segmentation is required. Even in English, naïve approaches (e.g., splitting on whitespace) are insufficient because of punctuation ("world!" → "world", "!") and multi-word entities (e.g., “New York” should ideally be two tokens, but as part of a name it represents one concept). A tokenizer uses rules or learned models to decide boundaries – for example, splitting contractions (“It’s” → “It”, “’s”) or handling abbreviations vs. sentence breaks (periods in “Dr.” should not end a sentence). By converting text to tokens, we normalize the input and enable consistent downstream analysis .
Another important purpose is that tokenization turns text into a form suitable for vectorization. Once we have tokens, we can create numerical representations such as one-hot encodings or embeddings. For example, after tokenizing, we might represent the sentence “Hello world!” as a vector count of words ({"Hello": 1, "world": 1} etc.). This step is what allows algorithms to ingest text as input features . In summary, tokenization structures raw text into units of meaning, making further processing like parsing or classification feasible and more accurate.
Example
As a concrete example in Python (using NLTK for simplicity):
from nltk.tokenize import word_tokenize, sent_tokenize
text = "Natural Language Processing (NLP) is exciting. Let's explore it!"
print(sent_tokenize(text))
# Output: ["Natural Language Processing (NLP) is exciting.", "Let's explore it!"]
print(word_tokenize(text))
# Output: ["Natural", "Language", "Processing", "(", "NLP", ")", "is", "exciting", ".", "Let", "'s", "explore", "it", "!"]
Here, the text is first split into two sentences. Each sentence is then split into word tokens, where punctuation and parentheses are isolated as separate tokens, and the contraction “Let’s” is split into "Let" and "'s". This illustrates how tokenization prepares text for analysis: we can now easily filter out punctuation tokens, look up the word “exciting” in a vocabulary, etc.
Advantages & Limitations
Advantages: Tokenization simplifies text processing by introducing structure. It preserves essential information (words and sentence boundaries) while eliminating irrelevant details (extra spaces, newline characters). By tokenizing, we make features like word frequency or position accessible. It also enables the use of algorithms that assume inputs are discrete symbols (words) rather than raw character streams . In effect, tokens serve as the basic units for tasks like parsing, part-of-speech tagging, and text classification, and a good tokenization will improve the performance of these tasks.
Limitations: A challenge is that tokenization can be ambiguous or language-dependent. There is no one-size-fits-all tokenizer. For instance, in English, deciding whether “U.S.” is one token or two (due to periods) is non-trivial. Tokenizers use heuristics or models, which sometimes make errors – e.g., splitting a named entity incorrectly or joining words that should be separate. Moreover, some languages (Chinese, Japanese) require complex segmentation algorithms because words aren’t delimited by spaces【3†img】. Even with spaces, polysemous words and contractions require context to tokenize correctly. In addition, tokenization can cause loss of information: e.g., the phrase “New York” as two tokens loses the information that it’s a single entity (which more advanced models might handle via multi-word expressions). Despite these issues, tokenization is usually a necessary compromise – its benefits for structure outweigh the minor losses, especially since later steps (like named entity recognition) can recover some of that information.
References
Guo, J. (1997). “Tokenization is the process of mapping sentences from character strings into strings of words.” (Computational Linguistics) .
Menzli, A. (2023). Importance of tokenization in NLP pipelines. Neptune Blog – Emphasizes tokenization as the first step that turns unstructured text into a structured numerical form .
Dr. Ernesto Lee (2025). Bag of Words and Text Preprocessing Guide. – Highlights that tokenization breaks text into words or sentences, enabling counting and downstream analysis .
Confidence
Confidence Level: 5/5. Tokenization is a well-understood, fundamental step in NLP. The definition and purposes provided are backed by standard references and widely accepted in the literature. All claims (e.g., the necessity of tokenization and its role in text preprocessing) are supported by references, and examples are consistent with known behavior of NLP tools. There is strong consensus in the NLP community on these points, so we are highly confident in the accuracy of this answer.
Variants for Tokenization
Factual Variant – “Why do we split text into sentences and words in NLP?”
Correct Answer: We split text into sentences and words to create tokens, which are the basic units of meaning that algorithms can process. Tokenization makes it easier for an NLP model to understand and analyze text by breaking a continuous string into manageable pieces (sentences and individual words) . Nearly all text processing tasks use tokenization as an initial step so that the text can be converted into a numerical form for modeling.
Distractor: “Because splitting text into tokens reduces the dataset size and improves runtime by discarding information.”
Explanation (Distractor): This answer is misleading. While tokenization can incidentally remove some characters (like extra spaces or punctuation), its primary purpose is not to reduce data size. In fact, tokenization often increases the number of elements (tokens) to process. The main goal is to structure the text (not to throw away information), so the focus is on breaking text into meaningful units rather than on runtime or size reduction (which the distractor incorrectly emphasizes).
Multi-hop Reasoning Variant – “How does tokenizing text facilitate machine learning models, and what might happen if we skipped tokenization when training a classifier?”
Correct Answer: Tokenization converts raw text into a structured format (a sequence of tokens) that machine learning models can interpret . By identifying sentence and word boundaries, tokenization enables the extraction of features like word frequencies or positions. If we skipped tokenization, the classifier would have to treat the entire text as one long string (or a sequence of characters), which would make it nearly impossible to learn meaningful patterns. For example, without tokenization, a spam filter couldn’t recognize the word “FREE” in an email; it would see a jumble of characters. Thus, tokenization is critical for the model to learn from text data, and skipping it would likely result in a model that fails to understand the input and generalizes poorly (overfitting to quirks of character sequences, or simply not learning anything useful).
Distractor: “Tokenization is only needed for languages without spaces; English doesn’t require tokenization for ML models.”
Explanation (Distractor): This is incorrect. While it’s true that some languages (like Chinese) mandate tokenization due to lack of spaces, tokenization is a crucial preprocessing step in all languages, including English. Even with spaces, English text has complexities (punctuation, contractions, multi-word names) that a simple whitespace split won’t handle well. Machine learning models trained on un-tokenized English text (either as raw strings or characters) perform much worse because they miss word-level patterns. The distractor wrongly assumes English whitespace is sufficient, ignoring the many benefits tokenization brings for English as well.
Ambiguous/Edge Case Variant – “In tokenization, how do we decide whether “New York” should be one token or two, and why does the purpose of tokenization complicate this decision?”
Correct Answer: Deciding if “New York” is one token or two is ambiguous because it’s a multi-word proper noun. The purpose of tokenization is to split text into useful units, but what’s “useful” can depend on context. Generally, a tokenizer will split “New York” into two tokens (“New”, “York”) by default (since it’s two words separated by space) . This is useful for a broad range of tasks because it treats each word as an independent feature. However, in some tasks (like geographic entity recognition), we might want “New York” treated as a single unit. Tokenization schemes can be adjusted (using rules or joining tokens) to handle such cases. The key point is that tokenization tries to balance breaking text into minimal meaning-carrying pieces and not losing multi-word context. In most general-purpose settings, it will output two tokens for “New York,” acknowledging the ambiguity but leaving it to later processing to recombine if needed. This illustrates that the purpose of tokenization (uniformly splitting text) can sometimes conflict with handling compound names, making it an edge case that advanced NLP pipelines address specially.
Distractor: “Always keep multi-word names like ‘New York’ as one token, because the purpose of tokenization is to preserve meaning at all costs.”
Explanation (Distractor): This statement overgeneralizes. While preserving meaning is important, the primary purpose of tokenization is to impose a consistent structure (even if that means breaking some multi-word expressions). If we “always” kept multi-word names as one token, we’d need complex heuristics for every case (e.g., “bank account” isn’t a name but is a compound concept). Tokenizers typically do not have knowledge of all multi-word names and will split on spaces. The distractor’s strategy could introduce more problems than it solves (e.g., treating every two capitalized words as one token could merge unrelated words). In reality, standard tokenization will split “New York,” and preserving or recovering the joint meaning is handled at later stages or via specialized tokenization only when appropriate. The distractor wrongly asserts an absolute rule that doesn’t exist in general tokenization practice.
|
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] |
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 2
|
zero_shot
|
What are n-gram language models and how are they useful in NLP?
| "Here's an explanation of n-gram language models and their utility in NLP, drawing on the provided c(...TRUNCATED)
|
[
9097,
1305,
4660
] | ["# L express and communicate, which develops in early ANGUAGE is a prominent ability in human being(...TRUNCATED)
| [[0.009204123169183731,-0.006637348793447018,0.08057183027267456,-0.020234864205121994,0.01367785967(...TRUNCATED)
| [-0.020002715289592743,-0.018173610791563988,0.06491893529891968,-0.03720715269446373,-0.01392249483(...TRUNCATED)
| [0.009771681390702724,-0.028391536325216293,0.08797500282526016,-0.04164384305477142,0.0123912058770(...TRUNCATED)
| "\nDefinition/Description\nAn n-gram language model is a probabilistic model of language that uses s(...TRUNCATED)
| [0.026327988132834435,-0.04495549574494362,0.08510898798704147,-0.02368880808353424,-0.0289780050516(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 3
|
zero_shot
|
What is the naive Bayes assumption and how does it relate to text classificatIon?
| "Here's a breakdown of the Naive Bayes assumption and its relevance to text classification, integrat(...TRUNCATED)
|
[
2385,
8239,
8053
] | ["### 5 Bayes optimal predictors \nIn this section, we develop the theory a theory for non-discrimi(...TRUNCATED)
| [[-0.03299742937088013,-0.03396319970488548,0.01658780872821808,0.00889715738594532,-0.0257661454379(...TRUNCATED)
| [-0.03811545670032501,-0.03579065576195717,0.04370281100273132,0.01571396365761757,0.051747668534517(...TRUNCATED)
| [-0.019608329981565475,-0.027029776945710182,0.11808760464191437,0.03290135785937309,0.0453944727778(...TRUNCATED)
| "\nDefinition/Description\nThe naïve Bayes assumption is the assumption that features (input attrib(...TRUNCATED)
| [-0.001857388997450471,-0.04025198519229889,0.09412525594234467,0.021481821313500404,0.0200799144804(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 4
|
zero_shot
| "What are some of the advantages and disadvantages of naive Bayes classifiers compared to logistic r(...TRUNCATED)
| "Here's a breakdown of the advantages and disadvantages of Naive Bayes classifiers compared to Logis(...TRUNCATED)
|
[
918,
5962,
697
] | ["### **6.2 Multi-class Logistic Regression** \nMulti-class logistic regression is the generalizati(...TRUNCATED)
| [[-0.05104101821780205,-0.10788612067699432,0.07671280205249786,0.022884398698806763,-0.009474359452(...TRUNCATED)
| [-0.04829598218202591,-0.048276133835315704,0.004509773105382919,0.03278204798698425,0.0172221064567(...TRUNCATED)
| [-0.04287531226873398,-0.036274105310440063,0.05644816905260086,0.03562040627002716,0.04958023130893(...TRUNCATED)
| "\nDefinition of the Comparison\nNaïve Bayes (NB) and Logistic Regression (LR) are both classifiers(...TRUNCATED)
| [-0.029843749478459358,-0.04755747318267822,0.062840536236763,0.02707415632903576,0.0062519009225070(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 5
|
zero_shot
| "What do we mean by \"features\" in the context of text classification? Give some examples of featur(...TRUNCATED)
| "Okay, let's delve into the concept of \"features\" in text classification and how they can be used (...TRUNCATED)
|
[
5033,
2273,
6834
] | ["##### 3.2.2 Text Categorization \nWe turn to text categorization tasks to determine whether softm(...TRUNCATED)
| [[-0.05227060243487358,0.03888903930783272,0.050341490656137466,-0.02052362821996212,-0.063328295946(...TRUNCATED)
| [-0.03803800046443939,-0.027116185054183006,0.028339989483356476,0.01096887793391943,-0.060643319040(...TRUNCATED)
| [-0.027826782315969467,-0.016586391255259514,0.06765002757310867,0.011164646595716476,-0.03203610330(...TRUNCATED)
| "\nDefinition/Description\nIn machine learning, features are individual measurable properties or cha(...TRUNCATED)
| [-0.0003641090006567538,-0.040918733924627304,0.03241101652383804,0.020151417702436447,-0.0485296510(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 6
|
zero_shot
| "What is the purpose of a test set in machine learning? Why do we need separate training and test se(...TRUNCATED)
| "## Test Sets in Machine Learning: Purpose and Necessity\n\nHere's a breakdown of the role of test s(...TRUNCATED)
|
[
4827,
4748,
6631
] | ["set **Q** *b* are first sampled from random *N* classes, and then train the parameters in *M* ( *.(...TRUNCATED)
| [[0.027410142123699188,-0.06911512464284897,-0.010143287479877472,0.03652532771229744,-0.00222943397(...TRUNCATED)
| [-0.016261819750070572,-0.03408167138695717,-0.02272798866033554,-0.0039716437458992004,-0.066584229(...TRUNCATED)
| [0.015581989660859108,-0.023816026747226715,-0.016249271109700203,0.025063976645469666,-0.0367690920(...TRUNCATED)
| "\nDefinition\nA test set is a subset of data set aside from the training process, used exclusively (...TRUNCATED)
| [0.037599314004182816,-0.04563342407345772,-0.012871906161308289,0.03249512240290642,-0.057992693036(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 7
|
zero_shot
| "What metrics could you use to evaluate the performance of a text classification model? Define accur(...TRUNCATED)
| "Here's a breakdown of metrics for evaluating text classification models, incorporating information (...TRUNCATED)
|
[
1706,
1098,
1562
] | ["### **2.5 Data Analysis** \nTo evaluate the performance of the DL-ReLU models, we employ\nthe fol(...TRUNCATED)
| [[-0.043372347950935364,-0.0552385076880455,-0.018689284101128578,0.015602236613631248,-0.0599072091(...TRUNCATED)
| [-0.06195545196533203,0.0031277018133550882,0.028214700520038605,-0.047715265303850174,-0.0391441099(...TRUNCATED)
| [-0.056562405079603195,-0.053965840488672256,0.043556977063417435,0.02463281713426113,-0.04317910596(...TRUNCATED)
| "\nEvaluation Metrics for Classification\nWhen evaluating a text classification model (or any classi(...TRUNCATED)
| [-0.03237234428524971,-0.08047349005937576,0.04319467023015022,0.0307672880589962,-0.052091535180807(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 8
|
zero_shot
| "How could you determine which features are most important or indicative for a logistic regression t(...TRUNCATED)
| "Here's an exploration of how to determine feature importance in a logistic regression model for tex(...TRUNCATED)
|
[
6841,
8474,
5335
] | ["### **4 Performance of Baseline Models** \nTo provide a point of reference for the CNN results, w(...TRUNCATED)
| [[-0.030143581330776215,0.0035113096237182617,0.047660015523433685,-0.009949791245162487,0.038143657(...TRUNCATED)
| [-0.07132473587989807,-0.01521983090788126,0.011749266646802425,-0.015355558134615421,-0.07356765121(...TRUNCATED)
| [-0.03320297226309776,-0.021981267258524895,0.05206489562988281,0.0068794661201536655,-0.00335775059(...TRUNCATED)
| "\nFeature Importance in Logistic Regression\nA logistic regression model is a linear model, which m(...TRUNCATED)
| [-0.031454406678676605,-0.025072110816836357,0.05386277288198471,0.015614205040037632,-0.02453402429(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 9
|
zero_shot
| "What is the bag-of-words representation and what are some of its limitations for text classificatio(...TRUNCATED)
| "Here's a breakdown of the bag-of-words representation, its limitations, and related concepts in tex(...TRUNCATED)
|
[
5488,
1569,
5494
] | ["### **1. Introduction** \nText classification and clustering play an important role\nin many appl(...TRUNCATED)
| [[-0.010748305357992649,-0.0090981749817729,0.0905781239271164,0.012348700314760208,-0.0452825501561(...TRUNCATED)
| [0.008314313367009163,0.0028699582908302546,0.05966051667928696,0.02533545345067978,-0.0409750901162(...TRUNCATED)
| [-0.0017602195730432868,-0.01932073011994362,0.07375891506671906,0.023040205240249634,-0.02214212343(...TRUNCATED)
| "\nDefinition / Description\nThe Bag-of-Words (BoW) model is one of the simplest and most widely use(...TRUNCATED)
| [0.014633245766162872,-0.022280924022197723,0.04460316151380539,0.011949770152568817,-0.038966253399(...TRUNCATED)
|
zero_shot_HW2_at_3_top_k
| 2
|
experiment_2
| 10
|
zero_shot
| "What is overfitting in machine learning? How could you tell if your text classification model is ov(...TRUNCATED)
| "Here's a breakdown of overfitting in machine learning, with a focus on text classification and meth(...TRUNCATED)
|
[
8077,
7013,
8075
] | ["avoid overfitting when training to within that threshold of convergence we require \n*D* ≳ (5 *(...TRUNCATED)
| [[-0.007690679281949997,-0.022567693144083023,-0.040892984718084335,-0.015242980793118477,-0.0266300(...TRUNCATED)
| [-0.021467646583914757,-0.030661460012197495,0.003845710540190339,-0.023320620879530907,-0.038202054(...TRUNCATED)
| [-0.013538396917283535,-0.0717194452881813,0.03803461790084839,0.01517021656036377,-0.03105535544455(...TRUNCATED)
| "\nDefinition/Description\nOverfitting is a common pitfall in machine learning where a model learns (...TRUNCATED)
| [-0.01095480564981699,-0.052822548896074295,0.020655972883105278,-0.00037668889854103327,-0.01470864(...TRUNCATED)
|
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