Pronunciation Ambiguities in Japanese Kanji
Year: 2023

Abstract:
Japanese writing is a complex system, and a large part of the complexity resides in the use of kanji. A single kanji character in modern Japanese may have multiple pronunciations, either as native vocabulary or as words borrowed from Chinese. This causes a problem for text-tospeech synthesis (TTS) because the system has to predict which pronunciation of each kanji character is appropriate in the context. The problem is called homograph disambiguation. To solve the problem, this research provides a new annotated Japanese single kanji character pronunciation data set and describes an experiment using the logistic regression (LR) classifier. A baseline is computed to compare with the LR classifier accuracy. This experiment provides the first experimental research in Japanese single kanji homograph disambiguation. The annotated Japanese data is freely released to the public to support further work.

heading: 1 Introduction
text: Japanese uses a mixed writing system with three distinct scripts and one romanization. Kanji 漢字 is the writing script that borrows directly from Chinese characters which were introduced in Japan from China through Korea from the third century CE. There are 2,136 commonly used kanji characters termed Joyo kanji in present-day Japanese. 1 A single kanji character in modern Japanese may have multiple pronunciations derived from the linguistic history of the kanji characters as either native vocabulary words or as terms borrowed from Chinese. For instance, the
1https://kanji.jitenon.jp/cat/joyo.html
kanji character 山 ‘mountain’ can be read as either the native Japanese word yama or the Chinesederived term san. The native Japanese pronunciations of the kanji character 文 ‘letter, sentence, writings’ are humi, aya, and kaza, while Chinese borrowed pronunciations are bun and mon. Because a kanji character has multiple pronunciations, to predict the appropriate pronunciation for each kanji character, a text-tospeech synthesis engine must select the appropriate reading. This is a form of homograph disambiguation.
This research is a computational study of Japanese kanji homograph disambiguation. Recent research in homograph disambiguation in Japanese is limited because of the lack of extensive data sets that include comprehensive pronunciations for the most commonly used kanji characters. The goal of this research is to fill this void, make new data sets to conduct the analysis of kanji characters with multiple pronunciations, and use the computational methodology to test the data set to lay a foundation for computational research on Japanese kanji homographs in the future.

heading: 1.1 Japanese writing scripts
text: The Japanese writing system uses three different scripts, Chinese characters (kanji), and two kana systems: hiragana and katakana, which are derivatives of Chinese characters. Hiragana resulted from the cursive style of writing Chinese characters, while katakana developed from the abbreviation of Chinese characters. Roughly speaking, kanji are used for content words such as nouns, stems of adjectives，and verbs, whereas hiragana is used for writing grammatical words
50
(case markers and other ‘small’ words). Katakana is almost exclusively used today to write foreign words and names such as Tennessee テネシー teneshii (Sproat, 2009: 47).
In addition to the three writing scripts that originate in Chinese characters, romaji is another phonetic writing script using the Roman alphabets. Thus, Japanese essentially has four ways to write the language. For instance, the word for ‘mountain’ can be written as 山 in kanji, as や
ま in hiragana, as ヤマ in katakana, and yama in romaji. For more details, see Zhang (2023: 4f.).
1.2 Kanji
The following sections introduce the kanji pronunciation ambiguities.
1.2.1 On readings and kun readings
Over time as Chinese characters were adapted to Japanese, the characters came to be associated with native Japanese words as well. For instance, the Chinese character for shān 山 was borrowed and used to write the newly created Japanese morpheme /san/. However, the Japanese already had the word yama ‘mountain’. The character 山 was also used to write the native word yama. Present-day Japanese has kept both terms for ‘mountain’ but uses them in different contexts. For instance, by itself, the signifier ‘mountain’ is usually referred to as yama, but Mt. Fuji is Fujisan. The kanji character 人 ‘people, person’ has two borrowed pronunciations: nin and jin, and one native Japanese pronunciation hito; the kanji character 者 ‘person’ has one borrowed pronunciation sha, and one native pronunciation mono. A majority of kanji characters have one or more Chinese-derived readings, and one or more native readings (Sproat et al., 2021). 2 The borrowed readings and native ones are known as on readings and kun readings, respectively. The readings in speech are not a problem, however, when given the written form first, for instance, both san and yama are written as 山, one must decide, depending on the context of each occasion, whether the character should be pronounced as san or yama. The multiple context-based pronunciations of a single kanji make Japanese text a challenge.
2There is a kanji category called 和製漢語 wasei kango ‘Japanese-made Chinese-character-based words’. Wasei kango are words that are composed of Chinese morphemes but were made by the Japanese rather than borrowed from Chinese. The items have kanji forms and most of them are

heading: 1.2.2 Multiple on readings
text: In general, a given kanji character may have several different Sino-Japanese readings reflecting the different stages at which the kanji character was borrowed from Chinese (Sproat, 2009: 47; Olinsky, 2000). Many Chinese words were assimilated into Japanese along with their characters and sounds during three unique historical periods. Each of these three periods of linguistic exchange are marked by a specific system of pronunciation. The three systems are Go’on ‘Go pronunciations’, Kan’on ‘Kan pronunciations’, and To’on ‘To pronunciations’. For instance, the kanji character行 has several on readings: gyo, ko, and an. For more details, see Zhang (2023: 7f.).
1.2.3 Multiple kun readings
The Chinese character borrowing has experienced at least three booms, and the cycles of kanji borrowing led to multiple usages for each single kanji character. In other words, one kanji character can hold multiple Japanese native readings with disparate associated meanings. For instance, the kanji character 生 has several kun readings each with a different meaning: iki ‘live, exist’, hae ‘grass grows’, nama ‘raw’, and u ‘to produce, give birth to’; 生 can also be read as ha, o, ki, inochi, ubu, and na.

heading: 1.2.4 Personal name readings
text: Personal name readings are a reading category different from on readings and kun readings. A kanji character has diverse readings in personal names which are different from its on readings and kun readings. For instance, the kanji character 一 has several personal name readings: i, osamu, ka, kazu, and katsu. For more details, see Zhang (2023: 9f.).

heading: 1.2.5 Reading ambiguities
text: Each single kanji character, generally, has at least one on and at least one kun reading. Because a kanji character has multiple readings, and each reading is used in different senses, when encountering a kanji character, one needs to figure out an appropriate contextual reading for the kanji character. For instance, the kanji character 行 is
read based on the kun reading rules, e.g., 蛯 ebi ‘shrimp’, 躾 shitsuke ‘upbringing’, and 凧 tako ‘kite’. A few of them have both on reading and kun reading, e.g., 雫 da (on reading), shizuku (kun reading) ‘driblet’, and 鱈 setsu (on reading), tara (kun reading) ‘cod’.
pronounced gyo in 修行 shugyo ‘ascetic practices,
training’, ko in 行動 kodo ‘action’, and an in 行
脚 angya ‘pilgrimage’.

heading: 1.3 TTS and TTS approaches
text: Text-to-speech synthesis (TTS) is a technology that allows written text to be output as speech. Because people are in fact very sensitive to both the words and the way they are spoken, the goals in building a high-quality TTS system should clearly get across the message and use a humanlike voice. These two goals of TTS are called intelligibility and naturalness (Taylor, 2009: 2-3).
The TTS problem is traditionally split into front-end and back-end systems. As one of the front-end system problems, the TTS system must predict the pronunciations of the words. For the in-vocabulary words with a single pronunciation, this requires only dictionary lookup. But for other types of words, for instance, homographs, because polysemous words are pronounced differently depending on the intended sense, one must analyze the context in which a kanji character occurs to select a contextually appropriate pronunciation (Gorman et al., 2018). This problem has been studied as homograph disambiguation, e.g., in English and a few other languages. A number of methods have been tried for several disambiguation tasks in NLP, including part of speech (POS) tagging and decision lists. Sproat et al. (1992) propose statistics of POS bigram or trigram to solve the problem and improve the disambiguation performance with words that have different POS taggers. Yarowsky (1994, 1997) presents decision list algorithms that combine the strengths of ngram taggers, Bayesian classifiers, and decision trees in a highly effective general-purpose decision procedure for lexical ambiguity resolution. Gorman et al. (2018) select a set of 163 homographs for the US English experiment and find that hybrid systems (making use of both rules and machine learning) are significantly more accurate than either hand-written rules or machine learning alone.

heading: 1.4 Japanese TTS homograph ambiguities
text: Japanese writing is a complex system, and a large part of the complexity resides in the reading of kanji characters. The trick in any case is to know which is the right reading, which makes reading Japanese text a challenge for the TTS system (Sproat, 2009: 47). As discussed in section 1.2.1, the kanji character 山 ‘mountain’ could be
pronounced either san or yama. The two pronunciations share the same meaning. However, the TTS system must do homograph disambiguation to find an appropriate pronunciation based on the contextual information of the kanji character. Two features are also related to the Japanese homograph disambiguation performance: word boundaries and formality. Because there is no word-boundary delimiter in Japanese, it is hard to identify a word. In the word segmentation process, if word boundaries cannot be identified correctly, it may lead TTS to incorrectly pronounce a string (Olinsky, 2000; Ooyama et al., 1987; Tesprasit et al., 2003). Therefore, problems of word boundary ambiguity and homograph ambiguity always occur together. Additionally, because Japanese writing is a combination of different scripts, kanji is used primarily for stems and hiragana is used for most inflectional endings and grammatical devices, word boundary discrimination can be simplified by detecting the transitions of the two different scripts. For instance, the string “現代の行政区分 ” ‘modern administrative divisions’ can be segmented at least into three tokens with the intervention of the hiragana の ‘possessive particle’ between the two
kanji clusters: “現代 ” ‘modern time’ and “行政
区分” ‘administrative divisions’. Katakana, on the other hand, is used primarily for phonetic renderings of foreign words, further reducing ambiguity. Thus, kanji can be considered the “harder” case for word segmentation (Olinsky, 2000). For instance, the kanji string “米国産業
界 ” can be segmented into two separate ways. The first kanji character holds different meanings and pronunciations based on the different segmentations. The first segmentation is 米
国 ‘America’ 産業 ‘Industry’ 界 ‘Realm’, in
which the first kanji character 米 is pronounced
Bei; the second segmentation is 米 ‘Rice’ 国産
‘Domestic production’ 業界 ‘Industry’, in
which the kanji character 米 is pronounced Kome. Taylor (2009: 46) discusses the homograph syntactic ambiguity using English sentence “Police help dog bite victim” which has at least two different possible syntactic patterns: (Police help dog) bite victim; and Police help (dog bite victim). The homograph syntactic ambiguities also exist in Japanese sentences. If not processed appropriately, it can hurt the performance of one of the TTS goals—intelligibility.
Japanese is famous for its politeness and formality. Some Japanese words have both informal and formal forms. Formal Japanese forms can additionally be divided into three categories: 丁寧語 teinei-go ‘polite form’, 尊敬
語 sonkei-go ‘honorific form’, and 謙譲語 kenjo-
go ‘humble form’. The kanji word 今日 ‘today’ has two pronunciations, the informal pronunciation is kyo, and the formal pronunciation is konnichi. Therefore, given the kanji word 今日, the system needs to analyze the formality based on the contextual information and select an appropriate pronunciation accordingly. Whether the system can do the contextual formality analysis perfectly or not will affect the achievement of the other TTS goal—naturalness. In addition to its importance in TTS applications, homograph disambiguation is relevant to automatic speech recognition (ASR) and is also a subset of word sense disambiguation (WSD) (Seale, 2021). Therefore, the task of kanji homograph disambiguation is not only important in improving the Japanese TTS performance but is also a crucial part of gaining high ASR and WSD accuracy.
However, Japanese kanji homograph disambiguation, to the best of the author’s knowledge, is not currently attested to in peerreviewed literature or otherwise published online. Also, there does not exist a well-developed data set that can support the research.
1.5 Labeling in Japanese kanji homographs
In TTS synthesis, the selection of the correct pronunciation of a text string occurs when a homograph is encountered (Seale, 2021). Homographs are pronounced differently depending on the intended sense, and the context provides enough clues for a homograph to select a contextually appropriate pronunciation (Gorman et al., 2018; Hearst, 1991). Yarowsky (1997) describes the techniques of English homograph disambiguation where each homograph is labeled originally by hand and a collection of features such as nearby content words. We manually label the pronunciations for each kanji homograph given a context and use machine learning methods to test the performance of the Japanese kanji homograph disambiguation. The data is released to the public for further experimentation by the NLP research community.

heading: 1.6 Research contributions
text: This research serves as the first academic work focused on Japanese kanji homograph disambiguation. Its contributions include publicizing the first single kanji pronunciation annotated data set, a typology of homographs with implications for both labeling and modeling and offering substantial language-specific resources to do Japanese homograph disambiguation. The data is released to the public for further experimentation by the NLP research community.
2 Data-driven kanji homograph research
This chapter introduces the Japanese kanji homograph data collecting, labeling, and modeling. Although very well respected at the current time, this research determined that the part of speech (POS) method is not compatible with Japanese homograph disambiguation. An explanation will be presented at the beginning of the chapter.

heading: 2.1 The reason for not using POS
text: With the increasing availability of annotated language data, several statistical part of speech (POS) tags have been developed which achieve high accuracy. Many prior work (Asahara et al., 2000; Brants, 2000; Denis, 2009; Gorman et al., 2018; Manning, 2011; Ratnaparkhi, 1997; Seale, 2021; Toutanova et al., 2000) uses POS features for disambiguating words. However, unlike homographs in some languages, the readings of kanji are generally not disambiguated by POS tags: firstly, most readings of kanji characters correspond to the same parts of speech, for instance, the kanji character 山 is a noun whether it is read as san or yama; secondly, some kanji characters cannot be assigned part of speech, for instance, the kanji character 文 is a component of
the nouns 文化 ‘culture’, 文法 ‘grammar’, and 文
学 ‘literature’, and the part of speech for 文 itself cannot be defined. Therefore, POS annotation was not adopted as an analyzer in this research. For more details, see Zhang (2023: 16f.).
2.2 Kanji homograph data
The goal of this research is to construct a way of determining an appropriate pronunciation for Japanese kanji homographs. For this purpose, a data set labeled the pronunciation of commonly used single kanji characters was constructed.

heading: 2.2.1 Data collection
text: The labeled single kanji homograph data set is constructed using the following data: Japanese dictionary Jiten 3 and Universal Dependencies (UD) Japanese-GSD.4 Jiten is an online Japanese dictionary. According to Jiten, as of June 2023, the number of recorded kanji characters is 27,693, and the total number of commonly used kanji characters is 2,136. This research collected each commonly used kanji character readings, including on readings, kun readings, and personal name readings. In addition, sentences from UD Japanese-GSD were combed for the context of commonly used kanji characters. These kanji characters and their pronunciations were combined with the Jiten kanji characters. The UD Japanese-GSD resource consists of sentences from Wikipedia and sentences which have been automatically split into words by IBM’s word segmenter (Asahara et al., 2018). The data set is segmented into 193,654 tokens and 8,100 sentences, and divided into training, development, and test sets. This research ignored the original splits for data collection.
2.2.2 Kanji homograph extractions
This research used Python libraries, data classes, and collections to extract the kanji homographs. The top 100 most commonly used kanji homographs in the combined UD Japanese-GSD data set were extracted. After this process, some kanji homographs were excluded. Firstly, it was determined that a kanji homograph with a frequency of 50 or more occurrences was optimal, because if there is not enough data for a given kanji homograph, we cannot build a good classifier. This resulted in 86 kanji homographs. Secondly, 18 semiotic classes were excluded. There are some semiotic classes, for instance, computer languages, email addresses, dates, times, telephone numbers, and postal addresses are much simpler than natural language, and problems will arise when we mix the natural language and those semiotic systems in the same signal and using the same characters to do so (Taylor, 2009: 33-34). In Japanese, the reading of a kanji character inside a number or date expression is different from reading a kanji character that is not a part of one of those expressions. For instance, the kanji homograph 一 ‘one’ has multiple readings.
When 一 stands alone, the reading is ichi; when 一
3https://jitenon.com/cat/common_kanji.php 4 The treebank is licensed under the Creative Commons License Attribution-ShareAlike 4.0 International.
is one part of the semiotic classes, for example, ichimai ‘one piece of’, ikko ‘one’, and hitotsu ‘one’, the reading will be ichi, itsu, and hito, respectively, depending on the following characters. The third exclusion ensures that each pronunciation must occur more than once and be at least 2% overall of the annotated data. Therefore, kanji homographs with only one pronunciation were removed so as not to bolster model scores, as the models would correctly pick the only pronunciation class available, and 36 kanji homographs removed due to pronunciation invariance. Thus, there were 32 kanji homographs retained. 1 kanji homograph was excluded due to Japanese formality. The kanji character 私 is a first-person singular pronoun that has two pronunciations: watashi and watakushi. There is only a slight difference between the two pronunciations, and distinguishing the two pronunciations requires subtle context. Therefore, it was excluded to avoid confusing the system. 2 kanji homographs were removed due to automatic kanji homograph extraction errors. 5 Data for a further 3 kanji homographs were removed because the number of examples that can be labeled was very small.6 For more details, see Zhang (2023: 18f.). After removing 74 kanji homographs, the remaining UD Japanese-GSD homograph data contains 26 unique homographs, and the 26 kanji homographs were modeled.
2.2.3 Kanji homograph pronunciation class size
Each kanji homograph in the data set has at least two pronunciations. 77% of the kanji homographs have two pronunciations, and 23% have three or four pronunciations. 70% of the kanji homographs with two pronunciations have one commonly used pronunciation, and the commonly used pronunciation is greater than or equal to 40% of the available data. One kanji homograph has the largest difference in pronunciation class size, with a ratio of 4:96, and one has a ratio of 49:51. On the other hand, four kanji homographs with more than two pronunciations have two commonly used pronunciations. Those two pronunciations accounted for around 90% of the available data and the ratio of the two is very close.
5The kanji homographs are見 and 出. 6The kanji homographs are 名, 位, and 次.

heading: 2.2.4 Data split redistribution
text: The total number of examples for the 26 kanji homographs in the data set is 1,903. These samples were split into 80% train, 10% dev, and 10% test. Stratified sampling was used as the default to maintain pronunciation class distribution among the splits. The stratified sampling method could be advantageous to sample each kanji homograph pronunciation category independently.

heading: 2.2.5 Data release
text: The new data sets were released for general use with the hope of helping to advance future research in Japanese and cross-lingual homographs. The data sets were released in two parts. First, 2,136 commonly used kanji homographs with their readings and reading types were released in a tab-separated values (TSV) file.7 Readings for each kanji homograph include on readings, kun readings, and personal name readings. An annotated UD Japanese-GSD—a data set of kanji homograph readings in context was also released.8 It includes sentences in which the target kanji homograph has been found.

heading: 2.3 Modeling
text: As the task of homograph disambiguation is to select a contextually appropriate pronunciation for a homograph, a logistic regression classifier was developed to make pronunciation predictions, and a baseline was computed to compare it against. While a baseline makes predictions that ignore the input features, the logistic regression classifier derives the input features from the homographs and the context surrounding them.
7https://github.com/wenzhang0222/thesis 8https://github.com/wenzhang0222/thesis

heading: 2.3.1 Baseline
text: A baseline was computed using the most frequent class label for each homograph, and then it was compared to the logistic regression classifier accuracy.

heading: 2.3.2 Logistic regression classifier
text: As the main task of homograph disambiguation is to select an appropriate pronunciation for a homograph given the context, a logistic regression (henceforth, LR) classifier was developed which considers the contextual features. In the development of the LR classifier, one LR classifier per-kanji was trained with the following n-gram features: tokens indexed one and two before and behind the homograph token, bigrams indexed immediately before and after the homograph token, and a skip-gram, the constituents of which surround the homograph token.9 Table 1 displays one example of the kanji homograph n-gram features: t shows the position of the target kanji homograph; t − 2 “も” and t −
1 “、” are the left two tokens, while t + 1 “が ”
and t + 2 “沢山 ” are the right two tokens. Each unigram t − 2, t − 1, t + 1, and t + 2; the previous bigram t − 2 and t − 1; the following bigram t + 1 and t + 2; and the skip-gram bigram t − 1 and t + 1 were extracted as the target token features.

heading: 2.4 Evaluation and analysis
text: Per-class accuracy of one of the models from the most performant model type is reviewed, and error analysis is done for all models.
9Writing script categories (kanji, hiragana, and katakana) for n-grams were also checked but not selected as one of the features because they did not help the overall performance.

heading: 2.4.1 Evaluation procedures
text: A baseline and LR classifier were trained and evaluated on the test set. Randomness can play a major role in the outcome of experiments and common sequence tagging tasks, the seed value for the random number generator can result in statistically significant differences for state-of-theart systems (Reimers et al., 2017). Because randomness is inherent in the model, different results will be obtained if it is run multiple times. To protect against the human selection of metrics from a particularly good run, the reported metrics were taken from the model with the median balanced accuracy from each set of five models’ performances. Hyperparameters were adjusted during training: L1 regularization, LIBLINEAR solver, and hyperparameter C = 10. Separate models were trained for each kanji homograph. In addition, development of the models was done using the train and dev splits, and metrics were reported on the test split.

heading: 2.4.2 Metrics
text: The accuracy of a model provides a measure of its predictions’ proximity to the correct values. Accuracy is determined in the range between 0
and 1. The performance of the models was evaluated using micro accuracy and macro accuracy.

heading: 2.4.3 Model performance
text: The micro and macro accuracies for the baseline and the LR classifier trained in this research are recorded in Table 2 — they show, on average, an increase between baseline and the non-baseline accuracies. The reasons are, firstly, logistic regression is famous for handling classification problems; secondly, L1 regularization was applied in the LR classifier, and it can handle both dense and sparse input.
2.4.4 Per-kanji homograph performance
Figure 1 shows the accuracies for each kanji homograph: the baseline accuracy is represented by light blue histograms and the LR accuracy is shown in dark blue histograms. While the baseline accuracies range from .43 to .86, the LR accuracies range from 0.57 to 1.00. Overall, the LR accuracies outperform baseline accuracies. For more details, see Zhang (2023: 29f.).

heading: 2.4.5 Error analysis
text: The following sections report error analysis based on the errors made by the baseline and the LR classifier.
Kanji geographical feature error analysis
Reading a kanji character in some place names is a problem due to many kanji characters in those place names do not follow the default or general reading rules (Jones et al., 2022). Most parts of
Japan have their own dialects that can be used for colloquial interactions. As a result, kanji reading in place names are sometime following local traditions or dialects. In this research, there is one kanji character that reflects the Japanese kanji geographical features. The kanji character is 町
‘town’. 町 has two pronunciations: the on reading
cho and the kun reading machi. The reading of 町 seems casual and it is largely influenced by its geographical location. Both 町 ’s baseline and LR accuracy are .67. For more details, see Zhang (2023: 31f.).
Fixed expression error analysis
Most kanji characters have a commonly used on reading and a commonly used kun reading. Generally, when on reading and kun reading share the same meaning, the context will select a reading based on the n-gram features and sentential formality. While kun reading is casual, on reading is more formal. However, in some fixed expressions, due to history and/or geographical reasons, on reading and kun reading will no longer be distinguished, and there is only one reading. For instance, the kanji character 生 has two commonly used pronunciations: one is the on reading sei, the other is the kun reading nama. When 生 means ‘live’, it can only be read as nama instead of sei. The LR classifier predicts 50% incorrectly in this case. Other examples can be found in Zhang (2023: 34f.).
Formality error analysis
The Japanese honorific system is well-developed, ranging from pronouncing a single kanji character in a specific context to choosing sentence patterns and expressions. We retained a mild formality kanji homograph to test whether the model can learn, and to what extent formality can be learned during training. The kanji character is 他 ‘others’.
他 can combine with the hiragana phrase その
‘that’ to make a fixed expression その他 ‘the others’, and it has two pronunciations in the combination: hoka and ta. The only difference between the pronunciations sonohoka and sonota is that sonohoka sounds more casual, which can be used in daily life conversations; sonata is formal, can be found in business expressions and official documents. The LR accuracy of the kanji character 他 is .82, and all the incorrect predictions are about the formality pronunciations
of 他 in the combination その他. This indicates that the LR classifier was not able to differentiate the Japanese formality robustly.
Pronunciation class size imbalanced error analysis
There are two kanji characters that each of which has four pronunciations, one is the kanji character 家 ‘home, house, family’ and one is the kanji
character 後 ‘later, back’. The character 家 ’s pronunciations are: ka, ke, ie, and ya. Among the 105 examples of the kanji character 家 , the pronunciation ka counts for 62%, ke is 19%, ie is 17%, and ya only counts for 2%. The LR classifier could not distinguish the two commonly used pronunciations: ka and ke and predicted all the ke to ka. LR also predicted ya to ka. Because the ratio of the pronunciation ya is very low, it can be assumed that features of the pronunciation ya were not fully learned by the LR classifier during training, and the LR classifier used the most frequent pronunciation ka to predict it. Other examples can be found in Zhang (2023: 35f.)

heading: 3 Discussion and conclusion
text: This research has been motivated by providing the first annotated Japanese single kanji pronunciation data set to solve Japanese kanji reading ambiguities. Although the pronunciation of Japanese kanji characters is a bottleneck on TTS performance, it has not been studied seriously due to the lack of reliable publicly available data. At the beginning of Chapter 2, this research addressed weaknesses in the use of part of speech (POS) as a mean of Japanese kanji homograph disambiguation and expounded on the source and methods of obtaining and processing the data. Some Japanese characteristics, for instance, rendaku, formality, and geographical features were taken in account when processing data.
Baseline and logistic regression (LR) classifier were used to examine the data performance. While the baseline can only obtain 67% prediction accuracy, the LR classifier, with the help of the ngram feature extractions, effective statistical analysis and regularization, improved the prediction accuracy to 83%. The following sections provide information about the known limitations of this research and directions for future research.

heading: 3.1 Known limitations and future research
text: As mentioned in Chapter 2, since data is annotated by the author in person, it may include some human error in labeling. However, this can be resolved through a review of the labels and the publication of an amended version. In addition, the sentence data is obtained from the Universal Dependencies (UD) Japanese-GSD data set, this is just one of eight UD Japanese corpora and other ones could be used to expand the data set. Also, this research treats the single kanji homographs as the target, and the work could undoubtedly be improved by expanding the research to kanji combinations. As discussed in Chapter 1, Japanese is one of the languages that lack word boundaries. Therefore, the first interesting point will be that when a kanji homograph is in a kanji combination or phrase, which kanji homographs will tie together to make a word to create a word boundary with other kanji homographs. Then the second point is how the new kanji combination can affect the pronunciation selection of those kanji homographs.
An anonymous reviewer suggests that we compare against the kanji disambiguation system embedded in MeCab. However, we leave this
comparison for future work.
Finally, due to time constraints, this research extracts n-gram features for the target kanji homographs. There will be other features that help analyze the context to improve the model performance.

heading: 3.2 Conclusion
text: This research has pioneered labeling for the task of Japanese kanji homograph disambiguation in text-to-speech applications. It contributes to providing the first public free kanji homograph annotated data. New data sets are offered to the research community to provide further research on this work. This research also provides a typology of homographs based on specific language features. The direction is set for future research in Japanese homograph studies and can be extended to research on the writing system of Chinese characters.

heading: Acknowledgements
text: I would like to express my gratitude to my advisor, Dr. Kyle Gorman, for his generous and professional guidance on my studies at the Graduate Center, City University of New York, for helping me to shape and fine-tune this research, and for sharing his expertise and time
with me throughout the paper. I deeply appreciate the opportunity to have worked together. I would also like to thank anonymous reviewers for their helpful feedback.

heading: Future Work
text: The direction is set for future research in Japanese homograph studies and can be extended to research on the writing system of Chinese characters