Lexical Analysis

"Lexical Analysis―Word Segmentation and Part-of-Speech Tagging."
NAIST NLP Lecture, November 13, 2024.

Transcript

1. NLP 2: Lexical Analysis ―Word Segmentation and Part-of-Speech Tagging Shohei Higashiyama Affiliate Assistant Professor@NAIST NLP NAIST Lecture (Nov. 13, 2024) Table of Contents: 1. Fundamental Concepts and Background 2. Some Methods for Lexical Analysis Tasks 3. Tokenization in the Neural NLP Era 4. The Usage of Foundational Lexical Analysis in Current NLP
2. Part 1: Fundamental Concepts and Background 2
3. Task ⚫What is a task in NLP? – A problem that a system or model needs to solve. – Defined by its input and output. 3 Example: Machine Translation (MT) 体調はどうですか？ How are you feeling? Text in the source language taichō wa dō desuka Text in the target language Example: Recognizing Textual Entailment (RTE) A pair of two texts One of three labels: “entailment,” “non-entailment,” or “neutral” She bought a new car. She has a car. [Premise] [Hypothesis] entailment
4. Representative Tasks in Lexical Analysis ⚫Tokenization ⚫Word Segmentation ⚫Part-of-Speech Tagging ⚫Morphological Analysis 4 [Notes] • The term Lexical Analysis is sometimes used to encompass various lexical-level linguistic analysis tasks, but it is not as well-established as individual task names. • This lecture regard tokenization as a lexical analysis task for convenience, but it not usually considered as such, as it does not necessarily identify linguistic units.
5. Tokenization and Word Segmentation ⚫Token/Tokenization – A token refers to a unit of text for processing in NLP. – Classical tokenizers (like NLTK) split a sentence into words and punctuation marks as tokens. ⚫Word Segmentation – A task of dividing a sentence into words. (This can be considered a type of tokenization.) – Typically performed for unsegmented languages w/o spaces, such as Japanese and Chinese. 5 言語は複雑だ 言語 は 複雑だ languages are complex Sentence Sequence of words gengo wa fukuzatsu-da Word boundaries Mr. Smith isn’t worried. Sentence Sequence of tokens Mr. Smith is n’t worried . * “は” is actually a topic marker.
6. Part-of-Speech Tagging ⚫Part-of-Speech (POS) – A grammatical category of a word, such as noun and verb, which indicates how it functions in a sentence. – Helps the system understand the roles of words within a sentence. ⚫POS Tagging – A task of assigning POS tags for words in a sentence. 6 Sequence of words [“Mr.”, “Smith”, “is”, “n’t”, “worried”, “.”] ADJ: adjective ADP: adposition ADV: adverb AUX: auxiliary CCONJ: coordinating conjunction DET: determiner INTJ: interjection NOUN: noun NUM: numeral PART: particle PRON: pronoun PROPN: proper noun PUNCT: punctuation SCONJ: subordinating conjunction SYM: symbol VERB: verb X: other Sequence of POS tags Universal POS tags [“PROPN”, “PROPN”, “AUX”, “PART”, “ADJ”, “PUNCT”]
7. Morphological Analysis ⚫Morpheme: The smallest linguistic unit that carries meaning. ⚫Definition of “Morphological Analysis” – Broad sense: A general term for analysis-focused morphological learning tasks – Narrow sense: A specific term refers to the combination of lemmatization and morphological tagging 7 runs Stem Cited From [Liu ‘21] Morphological analysis tasks (broad sense) Morphological generation tasks Morphemes * MSD: Morphosyntactic description * MSD tags can be regarded as fine-grained POS tags. run + s Word Suffix * The lemma and root are also “run.” Morphological learning tasks Morphological analysis (narrow sense)
8. Japanese Morphological Analysis ⚫Definition – Typically refers to a complex sentence-level task, involving word segmentation, POS tagging, and lemmatization (inflection processing). 8 昨日は楽しかった。 I had a good time yesterday. kinō wa tanoshikatta 昨日 は 楽しかっ た Input sentence Noun Particle Verb Suffix Output 昨日 は 楽しい た Word: Lemma: POS tag: [Notes] • Each “word” token actually correspond to a word, morpheme, or an intermediate unit. The segmentation granularity depends on the segmentation criterion (POS tag system). • Thus, in Japanese NLP, morhpmes and words are usually not strictly distinguished and are often used interchangeably.
9. Pipelined Task Flow in Traditional NLP ⚫In traditional NLP, lexical analysis tasks were essential steps. 9 Lexical Analysis Syntactic Analysis Discource Analysis Application- oriented task Semantic Analysis • Morphological analysis • POS tagging • Chunking • Dependency parsing • Semantic Role labeling • Word sense disambiguation • Anaphora resolution • Discourse parsing • Question answering • Machine translation etc. etc. etc. etc. etc. * Each application-oriented task does not necessarily require all the preceding lower-level tasks. Output/ Input Output/ Input Output/ Input Output/ Input Output Input
10. Other Fundamental Concepts ⚫Vocabulary – A set of words/tokens collected based on specific criteria. – An NLP model has a vocabulary of tokens that the model can process. ⚫Type vs. Token – Word type: Unique form of a word in a text/vocabulary. – Word token: Each instance of a word appeared in a text. ⚫Ambiguity – The possibility of multiple interpretations. – Word segmentation ambiguity can impact the performance of downstream tasks. 10 “a person has a pen” This sentence has 5 word tokens & 4 word types. 米原発熱海行き 米原|発|熱海|行き 米|原発|熱海|行き For Maibara Heat Sea U.S. Nuclear Power Plant to Atami 米原|発熱|海|行き Maibara-hatsu Atami-yuki ・These are machine translation outputs that I obtained on a previous day. ・米原 (Maibara) and 熱海 (Atami) are place names, so the third one is natural. Appropriate From Maibara to Atami * NLP faces many challenges due to various kinds of ambiguities in languages!
11. Part 2: Some Methods for Lexical Analysis Tasks 11 Somewhat Focusing on Word Segmentation
12. Progression of Methods for Word Segmentation and POS Tagging ⚫From around 2000 to the mid-2010s: – Statistical machine learning models were primarily used. ⚫From the mid-2010s to the early 2020s: – Neural network models were actively developed and have achieved substantial performance improvements (particularly in Chinese word segmentation). 12 cf. https://aclweb.org/aclwiki/POS_Tagging_(State_of_the_art) … (Omitted) BiLSTM-CRF Tagger [Huang+ ‘15] (Illustated outputs are labels for an NER task.) Note: Statistical models remain popular as practical tools, such as MeCab and Jieba. A lattice structure for the HMM-based POS tagger [Lee+ ‘00] [Liu+ ‘23] Survey on Chinese Word Segmentation
13. Statistical Methods for Japanese Morphological Analysis (JMA) ⚫Characteristics of statistical methods – Typical methods are lattice-based approaches relying on dictionaries. – Computationally efficient and fast. – Available off-the-shelf models can achieve high accuracy for well-formed text. ⚫MeCab [Kudo+ ‘04] – One of the de facto standard tools for JMA. – Based on Conditional Random Fields. 13 1. Construct the lattice for each input sentence while referring to the MA dictionary. 2. Search the best path using the Viterbi algorithm based on the trained model parameters. * It is because such models are trained with texts like news articles. A word lattice is a graph in which nodes represent words, and edges indicate that the two nodes can be connected.
14. Neural Methods for Word Segmentation ⚫Characteristics of neural methods – Often treat word segmentation as sequence labeling. – Language-independent and easily extensible to a multi-task model. – Can obtain benefit from powerful pretrained language models like BERT. 14 * Many NLP tasks other than word segmentation can also be formulated as sequence labeling. [奈, 良, に, は, 鹿, が, い, る] [B, E, B, B, B, B, B, E ] Input character tokens: Label sequence to be predicted: There are deer in Nara. B: Beginning of a word E: End of a word Nara ni-wa shika-ga iru Sequence labeling: a type of a task that predicts a label sequence for an input token sequence 奈良|に|は|鹿|が|いる * Popular tag schemas: BE (=0/1), BIE, BIES, etc.
15. Neural Methods for Word Segmentation ⚫A character-word hybrid model [Higashiyama+ ‘19] – Relied a BiLSTM-CRF, which was the de facto standard architecture for neural sequence labeling. – Achieved state-of-the-art accuracy for both Japanese and Chinese word segmentation. • By changing the training data, the same model can be used for different languages. 15
16. Neural Methods for Word Segmentation ⚫Model comparison on various Japanese texts – MeCab achieved high accuracy for GEN (general) domain. • The model was trained on GEN domain data. – BERT achieved high accuracy for many domains. • The model was pretrained on Japanese Wikipedia texts and fine-tuned on GEN domain data. 16 Acucracy (F1 scores) of Japanese word segmenters on various domains (“dom.”) [Higashiyama+ ‘22] Neural methods (based on pretrained language models) have the advantage of robustness to domain shift. * BERT [Devlin+ ‘19] is a powerful neural NLP model and a prominent example of a masked language model (MLM). ENE to EMR: Scientific documents. DIE to PRM: Government documents. TBK to VRS: Other documents.
17. Practical Systems for Japanese Word Segmentation ⚫ Statistical methods, particularly MeCab, are still widely used for high processing speed. ⚫ Juman++ V2 [Tolmachev+ ‘20] utilizes a recurrent neural network (RNN) language model to achieve high accuracy while maintaining practical processing speed. 17 Rule-based method Statistical method Juman++: Statistical/Neural hybrid method Accuracy (F1 scores) of Japanese word segmenters on web text (KWDLC data) Processing speed fo Japanese word segmenters * Results are from [Tolmachev+ ‘20]. * Vaporetto and vibrato, implementations of KyTea and MeCab like tokenizers, achieve faster tokenization than them.
18. Part 3: Tokenization in the Neural NLP Era 18
19. What are Suitable Token Units for Various NLP Tasks? ⚫Word Segmentation: Character – Words cannot be used before predicting them. 19 Example inputs/outputs [奈, 良, に, は, 鹿, が, い, る] [B, E, B, B, B, B, B, E ] Input character tokens: Label sequence to be predicted: There are deer in Nara. B: Beginning of a word E: End of a word Nara ni-wa shika-ga iru
20. What are Suitable Token Units for Various NLP Tasks? ⚫Most downstream tasks: Word (?) – Using words seems efficient and easy. – Using characters leads to long token sequences, which increases computation costs and raises modeling difficulty. 20 [I, _, d, o, _, n, o, t, _, l, i, k, e, _ , t, h, i, s, _, m, o, v, i, e] Example inputs/outputs for Sentiment Analysis [I, do, not, like, this, movie] When using character tokens: When using word tokens: Label to be predicted: negative A system is required to grasp the information expressed by the combination of these tokens. * Previous character-level models have shown lower accuracy than (sub)word-level models [Al-Rfou+ ‘19]. However, it has been demonstrated that Transformers with deep layers and a large number of parameters, even at the byte level, can achieve performance competitive with (sub)word-level models [Choe+ ‘19].
21. Problems of Using Word Tokens in Downstream Tasks ⚫A large vocabulary (with size |V|) leads to: – Expensive (infeasible) computation, particularly for language generation models – A large number of model parameters ID Token 0 [UNK] 1 the 2 is … … 50000 placid … … Corpus (Set of texts) Model’s vocabulary Words appeared in the corpus 21 Neural network model To keep the vocabulary size practical (typically <100K), a special unknown token is used to represent all words outside the vocabulary, but this is not ideal. A large corpus may include millions of word types. RNN with attention (Cited from Dive into Deep Learning) When |V|=32,000 and dim=512, word embedding parameters account for 25% of GRU model.
22. A Solution: Subword ⚫Subword tokenization – Breaks down less common words into subword tokens based on statistical criteria. ⚫Pros: – Vocabulary size is controllable as a model’s hyper-parameter. – Unknown words are rare: Most words can be represented by combinations of subwords. ⚫Cons: – Subwords do not align with the boundaries of linguistically meaningful units (e.g., morphemes). – A tokenizer depends on specific training data, leading to less portability. 22 There have been significant advancements in NLP technologies. [“there”, “have”, “been”, “significant”, “advancement”, “##s”, “in”, “nl”, “##p”, “technologies”, “.”] (tokenizer: google-bert/bert-base-multilingual-uncased) Input text: Output tokens: The symbol “##” represents non-initial token in a word (used in WordPiece tokenizer) These drawbacks are not often practically critical, and subwords have become the de facto standard. * word segmenters also have this limitation.
23. Subword Tokenization Algorithms ⚫Three popular algorithms: – WordPiece [Schuster+ ‘12] – Byte Pair Encoding (BPE) [Sennrich+ ‘16] – Unigram Language Model (LM) [Kudo ‘18] ⚫Two phases for subword tokenization – Train a tokenization model from a training corpus. – Use the model to tokenize new text. 23 cf. https://huggingface.co/docs/transformers/tokenizer_summary. Explained in detail in later slides.
24. Byte Pair Encoding (BPE) ⚫Training algorithm 1. Create an initial small subword vocabulary of all characters in the training corpus. 2. Merge the two consecutive subwords that occur most frequently in the corpus. – The tokenizer learns the merge rule. 3. Repeat step 2. until the vocabulary reaches the desired size. 24 ("hug", 10), ("pug", 5), ("pun", 12), ("bun", 4), ("hugs", 5) ["b", "g", "h", "n", "p", "s", "u"] ("h" "u" "g", 10), ("p" "u" "g", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "u" "g" "s", 5) ("h" "ug", 10), ("p" "ug", 5), ("p" "u" "n", 12), ("b" "u" "n", 4), ("h" "ug" "s", 5) ["b", "g", "h", "n", "p", "s", "u“, “ug”] Corpus: Corpus (subword-based): Vocabulary: Corpus (subword-based): Vocabulary: Indicates that “hugs” occurs five times, and so on. Merge “u” and “g” (frequency: 20) New subword Merge “u” and “n” (frequency: 16) … Example is from Hugging Face
25. Unigram Language Model 25 Likelihood decrease or loss for sm ⚫Training algorithm 1. Create an initial large subword vocabulary V consisting of a reasonably large number of possible subwords from the training corpus D = {x(i)}. 2. Calculate the likelihood decrease for each subword sm ∈ V when it is removed, and Remove the top k (e.g., 20) percent of subwords that most decrease the likelihood. – Single-character subwords are retained regardless of their effect on the loss to avoid the out-of-vocabulary problem. 3. Repeat step 2. until the vocabulary reaches the desired size. Probability P(y) of segmentation y = (y1 , ..., y|y| ) All sentences D = {x(i)} Likelihood over D xi ＝ “Hello world” Set of all possible segmentation y based on the vocab V P(S(x(i)), V) y = “▁Hell/o/▁world” P(y) y = “▁H/ello/▁world” P(y) y = “▁He/llo/▁world” P(y) y = “▁/He/l/l/o/▁world” P(y) y = “▁H/el/l/o/▁/world” P(y) … Sum
26. Pre-tokenization-free Tokenizer ⚫Sentencepiece [Kudo+ ‘18] – Subword tokenization tool (￢algorithm) that implemented BPE and Unigram LM. – Enabled language-independent tokenization by treating all characters, including whitespace, as usual symbols. 26 “Hello world.” “▁Hello▁world” “Hello”, “world” Sentencepiece Other tokenizers Input text ... ... Assume that an input can be separated by whitespaces. This necessitates pre-tokenization for unsegmented languages. The input is processed “as is.” Apply subword tokenization alogrithm * The beginning of sentence and whitespaces are replaced by a meta character “▁” Language-specific pre-tokenization like word segmentation is not required for unsegmented languages.
27. Part 4: The Usage of Foundational Lexical Analysis in Current NLP 27 Somewhat Focusing on Word Segmentation
28. How Lexical Analysis is Currently Used: A Case of Japanese NLP ⚫ Direct subword tokenization from raw text performs well in downstream tasks. ⚫ Two-step tokenization is also often adopted. 28 ▁|今日|は|京王|線で|天気|の|子の|聖地|新宿|へ ▁今日は|京王|線|で|天気|の|子|の|聖地|新宿|へ ➢ bert-japanese’s tokenizer: Unigram LM ➢ llm-jp-3-1.8b-instruct’s tokenizer: Unigram LM with MeCab pre-tokenization 今日は京王線で天気の子の聖地新宿へ Today I’m heading to Shinjuku, a location featured in Weathering with You, via the Keio Line. Input: kyō wa keiō-sen de tenki-no-ko no seichi shinjuku e Train line name Place name Anime title Named entities: Specific real-world objects/concepts * “▁” is a symbol representing the beginning of a non-spaced sequence. Token boundaries do not align with word boundaries, particularly for named entities. First perform word segmentation and then build a subword vocabulary based on obtained words. Direct subword tokenization Two-step tokenization This reduces cases of boundary conflicts. * A recommended method of combining Sentencepiece and MeCab is explained here (in Japanese).
29. How Downstream Task Accuracy Differ by Tokenizers? 29 * Results are from [Fujii+ ‘23]. Unigram achieved robust accuracy w/o word segmentation (except for NER) Sentiment analysis Sentence similarity Entailment recognition Question answering Dependency parsing Two-step: Use word segmenter as pre-tokenizer Direct: Subword tokenizer only Named entity recognition ⚫It depends on tasks, but using words often produces good results (in Japanese NLP). – Direct subword tokenization led to a large performance drop for Named Entity Recognition.
30. A Case of a Morphologically Rich Language: Kinyarwanda ⚫KinyaBERT – Uses two-tier BERT architecture (morpheme and sentence-level encoders) designed specifically for this language. – Achieved high accuracy across tasks. 30 MRPC QNLI RTE SST-2 STS-B WNLI NER NEWS Cited from [Nzeyimana+ ‘22] * The results on the test data. Words and morphemes of Kinyarwanda Models with BPE or morpheme tokenization yielded lower accuracy than KinyaBERT on most tasks.
31. What Tokenization is Used in State-of-the-Art LLMs? ⚫Byte-level BPE – The vocabulary is initialized with 256 UTF-8 byte tokens, and new tokens are added by merging existing tokens. – Completely eliminates the issue of unknown words. Adopted in: – English-centric LLMs: GPT-4o, Llama 3 – Chinese-centric LLMs: Qwen 2 31 https://belladoreai.github.io/llama3-tokenizer-js/example-demo/build/ Llama 3 tokenizer A rare character was split into byte token sequences. * In UTF-8 encoding, each byte (=8 bits) can represent 28=256 possible values.
32. What Tokenization is Used in State-of-the-Art LLMs? ⚫Two-step tokenization (word and subword) – Japanese-centric LLM • LLM-jp: MeCab+JumanDIC → Unigram LM – Japanese-centric LLM extended from English-centric LLM • Swallow: MeCab+UniDic → BPE 32 日|本|語|の|自|然|言|語|<0xE5>|<0x87>|<0xA6>|理 日本|語|の|自然|言語|処理 ➢ Before vocabulary expansion (Base model: Llama 2) ➢ After vocabulary expantion (Swallow-7b-hf) 日本語の自然言語処理 12 tokens 6 tokens Japan|word or language|(no-particle)|natural |language|processing Input: nihon go-no shizen gengo shori Efficient tokenization is important to minimize costs. [Fujii+ ‘24] Swallow’s vocabulary expansion improved Japanese text generation efficiency up to 78%, while almost maintaining accuracy in downstream tasks. Byte sequence Tokenizers not optimized for non-Latin characters increase training/inference costs for non-Latin languages.
33. Conclusion: Current and Future of Lexical Analysis Tasks ⚫Decreased importance in the neural era – End-to-end neural systems perform well without explicit linguistic information. – If the goal is to achieve high accuracy in downstream NLP tasks, considering lexical (and higher-level) linguistic analysis is often unnecessary. • However, tokenization-related tasks may enhance downstream task performance to some extent in unsegmented and morphologically rich languages. ⚫Unchanging usefulness as fundamental tools – There is stable demand for constructing new corpora with linguistic information for fields such as (computational) linguistics. – If the goal is to obtain lexical analysis results, lexical analysis systems are essential. ⚫Future research for lexical analysis – Research opportunities remain since the performance of current systems is not perfect. – Academic research in this area will likely continue, though on a small scale. 33 * For conducting further research on established lexical analysis tasks, it is important to explain how critical the errors of existing systems are for a target use case.
34. Appendix 34
35. How Actively has Lexical Analysis been Researched? ⚫Yearly changes in the number of ACL Anthology papers containing keywords and target language names in titles. 35 (From 1991 to Oct 2024.)  Keywords: word segmentation/segmenter  Keywords: part(s) of speech, pos 199 34 18 15 9 8 6 103 256 15 10 9 6 Total count for each language (from 1952 to present) • “None” indicates cases where no language name is included in paper titles. (Those papers can correspond to research focused on specific languages.) • Each language name indicates cases where both any of the keywords and the language name are included. Chinese would account for many of “none”. Many of “none” would focus on Enligsh (and other languages).
36. How Actively has Lexical Analysis been Researched? ⚫Yearly changes in the number of ACL Anthology papers containing keywords and target language names in titles. 36 (From 1991 to Oct 2024.)  Keywords: word segmentation/segmenter  Keywords: part(s) of speech, pos 199 34 18 15 9 8 6 103 256 15 10 9 6 Total count for each language (from 1952 to present) • “None” indicates cases where no language name is included in paper titles. (Those papers can correspond to research focused on specific languages.) • Each language name indicates cases where both any of the keywords and the language name are included. Chinese would account for many of “none”. Many of “none” would focus on Enligsh (and other languages). Research on these lexical tasks has been relatively active from around 2000 to 2020. Specifically, the majority of word segmentation research has focused on Chinese. Both word segmentation and POS tagging (a total of 618 papers) have been less active compared to, for example, machine translation (5,475 papers). * These numbers are rough estimates based on title-keyword match. The actual number of related research papers is likely to be higher.
37. Optional Reading Materials ⚫Tokenization – [Mielke+ 2021] Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP ⚫Morphological Analysis – [Liu 2021] Computational Morphology with Neural Network Approaches – [Baxi+ 2024] Recent advancements in computational morphology : A comprehensive survey ⚫Part-of-Speech Tagging – [He+ 2020] A Survey on Recent Advances in Sequence Labeling from Deep Learning Models • Note: This paper includes citations for state-of-the-art POS tagging methods (at the time of publication). – [Chiche+ 2022] Part of speech tagging: a systematic review of deep learning and machine learning approaches • Note: This article mainly reviews journal articles published between 2017-2021 and rarely includes international conference papers, especially those from ACL-related conferences. The coverage of state-of- the-art methods is limited, but it seems useful for understanding a rough trend in POS tagging research. 37
38. Optional Reading Materials ⚫Japanese Morphological Analysis – [Unno 2011] 形態素解析の過去・現在・未来 (In Japanese) – [Kaji 2013] 日本語形態素解析とその周辺領域における最近の研究動向 (in Japanese) – [Kudo 2018] 形態素解析の理論と実装 (Book; In Japanese) – [Higashiyama 2022] (slides) Word Segmentation and Lexical Normalization for Unsegmented Languages • Note: Sections 2 and 7 discusses preliminaries and future prospectives for neural word segmentation. ⚫Chinese Word Segmentation – [Fu+ 2020] RethinkCWS: Is Chinese Word Segmentation a Solved Task? – [Liu+ 2023] Survey on Chinese Word Segmentation ⚫Multilingual Projects for Resource Construction – UniMorph, https://unimorph.github.io/ – Universal Dependencies, https://universaldependencies.org/ 38