A Lexicalized Tree Adjoining Grammar for Thai

PACLIC 23 / City University of Hong Kong / 3-5 December 200

General methods for fine-grained morphological and syntactic disambiguation

We present methods for improved handling of morphologically rich languages (MRLS) where we define MRLS as languages that are morphologically more complex than English. Standard algorithms for language modeling, tagging and parsing have problems with the productive nature of such languages. Consider for example the possible forms of a typical English verb like work that generally has four four different forms: work, works, working and worked. Its Spanish counterpart trabajar has 6 different forms in present tense: trabajo, trabajas, trabaja, trabajamos, trabajáis and trabajan and more than 50 different forms when including the different tenses, moods (indicative, subjunctive and imperative) and participles. Such a high number of forms leads to sparsity issues: In a recent Wikipedia dump of more than 400 million tokens we find that 20 of these forms occur only twice or less and that 10 forms do not occur at all. This means that even if we only need unlabeled data to estimate a model and even when looking at a relatively common and frequent verb, we do not have enough data to make reasonable estimates for some of its forms. However, if we decompose an unseen form such as trabajaréis `you will work', we find that it is trabajar in future tense and second person plural. This allows us to make the predictions that are needed to decide on the grammaticality (language modeling) or syntax (tagging and parsing) of a sentence. In the first part of this thesis, we develop a morphological language model. A language model estimates the grammaticality and coherence of a sentence. Most language models used today are word-based n-gram models, which means that they estimate the transitional probability of a word following a history, the sequence of the (n - 1) preceding words. The probabilities are estimated from the frequencies of the history and the history followed by the target word in a huge text corpus. If either of the sequences is unseen, the length of the history has to be reduced. This leads to a less accurate estimate as less context is taken into account. Our morphological language model estimates an additional probability from the morphological classes of the words. These classes are built automatically by extracting morphological features from the word forms. To this end, we use unsupervised segmentation algorithms to find the suffixes of word forms. Such an algorithm might for example segment trabajaréis into trabaja and réis and we can then estimate the properties of trabajaréis from other word forms with the same or similar morphological properties. The data-driven nature of the segmentation algorithms allows them to not only find inflectional suffixes (such as -réis), but also more derivational phenomena such as the head nouns of compounds or even endings such as -tec, which identify technology oriented companies such as Vortec, Memotec and Portec and would not be regarded as a morphological suffix by traditional linguistics. Additionally, we extract shape features such as if a form contains digits or capital characters. This is important because many rare or unseen forms are proper names or numbers and often do not have meaningful suffixes. Our class-based morphological model is then interpolated with a word-based model to combine the generalization capabilities of the first and the high accuracy in case of sufficient data of the second. We evaluate our model across 21 European languages and find improvements between 3% and 11% in perplexity, a standard language modeling evaluation measure. Improvements are highest for languages with more productive and complex morphology such as Finnish and Estonian, but also visible for languages with a relatively simple morphology such as English and Dutch. We conclude that a morphological component yields consistent improvements for all the tested languages and argue that it should be part of every language model. Dependency trees represent the syntactic structure of a sentence by attaching each word to its syntactic head, the word it is directly modifying. Dependency parsing is usually tackled using heavily lexicalized (word-based) models and a thorough morphological preprocessing is important for optimal performance, especially for MRLS. We investigate if the lack of morphological features can be compensated by features induced using hidden Markov models with latent annotations (HMM-LAs) and find this to be the case for German. HMM-LAs were proposed as a method to increase part-of-speech tagging accuracy. The model splits the observed part-of-speech tags (such as verb and noun) into subtags. An expectation maximization algorithm is then used to fit the subtags to different roles. A verb tag for example might be split into an auxiliary verb and a full verb subtag. Such a split is usually beneficial because these two verb classes have different contexts. That is, a full verb might follow an auxiliary verb, but usually not another full verb. For German and English, we find that our model leads to consistent improvements over a parser not using subtag features. Looking at the labeled attachment score (LAS), the number of words correctly attached to their head, we observe an improvement from 90.34 to 90.75 for English and from 87.92 to 88.24 for German. For German, we additionally find that our model achieves almost the same performance (88.24) as a model using tags annotated by a supervised morphological tagger (LAS of 88.35). We also find that the German latent tags correlate with morphology. Articles for example are split by their grammatical case. We also investigate the part-of-speech tagging accuracies of models using the traditional treebank tagset and models using induced tagsets of the same size and find that the latter outperform the former, but are in turn outperformed by a discriminative tagger. Furthermore, we present a method for fast and accurate morphological tagging. While part-of-speech tagging annotates tokens in context with their respective word categories, morphological tagging produces a complete annotation containing all the relevant inflectional features such as case, gender and tense. A complete reading is represented as a single tag. As a reading might consist of several morphological features the resulting tagset usually contains hundreds or even thousands of tags. This is an issue for many decoding algorithms such as Viterbi which have runtimes depending quadratically on the number of tags. In the case of morphological tagging, the problem can be avoided by using a morphological analyzer. A morphological analyzer is a manually created finite-state transducer that produces the possible morphological readings of a word form. This analyzer can be used to prune the tagging lattice and to allow for the application of standard sequence labeling algorithms. The downside of this approach is that such an analyzer is not available for every language or might not have the coverage required for the task. Additionally, the output tags of some analyzers are not compatible with the annotations of the treebanks, which might require some manual mapping of the different annotations or even to reduce the complexity of the annotation. To avoid this problem we propose to use the posterior probabilities of a conditional random field (CRF) lattice to prune the space of possible taggings. At the zero-order level the posterior probabilities of a token can be calculated independently from the other tokens of a sentence. The necessary computations can thus be performed in linear time. The features available to the model at this time are similar to the features used by a morphological analyzer (essentially the word form and features based on it), but also include the immediate lexical context. As the ambiguity of word types varies substantially, we just fix the average number of readings after pruning by dynamically estimating a probability threshold. Once we obtain the pruned lattice, we can add tag transitions and convert it into a first-order lattice. The quadratic forward-backward computations are now executed on the remaining plausible readings and thus efficient. We can now continue pruning and extending the lattice order at a relatively low additional runtime cost (depending on the pruning thresholds). The training of the model can be implemented efficiently by applying stochastic gradient descent (SGD). The CRF gradient can be calculated from a lattice of any order as long as the correct reading is still in the lattice. During training, we thus run the lattice pruning until we either reach the maximal order or until the correct reading is pruned. If the reading is pruned we perform the gradient update with the highest order lattice still containing the reading. This approach is similar to early updating in the structured perceptron literature and forces the model to learn how to keep the correct readings in the lower order lattices. In practice, we observe a high number of lower updates during the first training epoch and almost exclusively higher order updates during later epochs. We evaluate our CRF tagger on six languages with different morphological properties. We find that for languages with a high word form ambiguity such as German, the pruning results in a moderate drop in tagging accuracy while for languages with less ambiguity such as Spanish and Hungarian the loss due to pruning is negligible. However, our pruning strategy allows us to train higher order models (order > 1), which give substantial improvements for all languages and also outperform unpruned first-order models. That is, the model might lose some of the correct readings during pruning, but is also able to solve more of the harder cases that require more context. We also find our model to substantially and significantly outperform a number of frequently used taggers such as Morfette and SVMTool. Based on our morphological tagger we develop a simple method to increase the performance of a state-of-the-art constituency parser. A constituency tree describes the syntactic properties of a sentence by assigning spans of text to a hierarchical bracket structure. developed a language-independent approach for the automatic annotation of accurate and compact grammars. Their implementation -- known as the Berkeley parser -- gives state-of-the-art results for many languages such as English and German. For some MRLS such as Basque and Korean, however, the parser gives unsatisfactory results because of its simple unknown word model. This model maps unknown words to a small number of signatures (similar to our morphological classes). These signatures do not seem expressive enough for many of the subtle distinctions made during parsing. We propose to replace rare words by the morphological reading generated by our tagger instead. The motivation is twofold. First, our tagger has access to a number of lexical and sublexical features not available during parsing. Second, we expect the morphological readings to contain most of the information required to make the correct parsing decision even though we know that things such as the correct attachment of prepositional phrases might require some notion of lexical semantics. In experiments on the SPMRL 2013 dataset of nine MRLS we find our method to give improvements for all languages except French for which we observe a minor drop in the Parseval score of 0.06. For Hebrew, Hungarian and Basque we find substantial absolute improvements of 5.65, 11.87 and 15.16, respectively. We also performed an extensive evaluation on the utility of word representations for morphological tagging. Our goal was to reduce the drop in performance that is caused when a model trained on a specific domain is applied to some other domain. This problem is usually addressed by domain adaption (DA). DA adapts a model towards a specific domain using a small amount of labeled or a huge amount of unlabeled data from that domain. However, this procedure requires us to train a model for every target domain. Instead we are trying to build a robust system that is trained on domain-specific labeled and domain-independent or general unlabeled data. We believe word representations to be key in the development of such models because they allow us to leverage unlabeled data efficiently. We compare data-driven representations to manually created morphological analyzers. We understand data-driven representations as models that cluster word forms or map them to a vectorial representation. Examples heavily used in the literature include Brown clusters, Singular Value Decompositions of count vectors and neural-network-based embeddings. We create a test suite of six languages consisting of in-domain and out-of-domain test sets. To this end we converted annotations for Spanish and Czech and annotated the German part of the Smultron treebank with a morphological layer. In our experiments on these data sets we find Brown clusters to outperform the other data-driven representations. Regarding the comparison with morphological analyzers, we find Brown clusters to give slightly better performance in part-of-speech tagging, but to be substantially outperformed in morphological tagging

Assessment of Pre-Trained Models Across Languages and Grammars

We present an approach for assessing how multilingual large language models (LLMs) learn syntax in terms of multi-formalism syntactic structures. We aim to recover constituent and dependency structures by casting parsing as sequence labeling. To do so, we select a few LLMs and study them on 13 diverse UD treebanks for dependency parsing and 10 treebanks for constituent parsing. Our results show that: (i) the framework is consistent across encodings, (ii) pre-trained word vectors do not favor constituency representations of syntax over dependencies, (iii) sub-word tokenization is needed to represent syntax, in contrast to character-based models, and (iv) occurrence of a language in the pretraining data is more important than the amount of task data when recovering syntax from the word vectors.Comment: Accepted at IJCNLP-AACL 202

Automatic Generation of Morpheme Level Reordering Rules for Korean to English Machine Translation

학위논문 (석사)-- 서울대학교 대학원 : 언어학과, 2017. 2. 신효필.Word order is one of the main challenges that machine translation systems must overcome when dealing with any linguistically divergent language pair, such as Korean and English. Statistical machine translation (SMT) models are often insufficient at long distance reordering due the distortion penalties they impose.Rule-based systems, on the other hand, are often costly, in both time and money, to build and maintain. The present research proposes a new hybrid approach for Korean to English machine translation. While previous approaches have focused on the word, our approach considers the morpheme as the basic unit of translation for this language pair. We begin by developing a classification model to disambiguate Korean functional morphemes based on alignment pairs and context feature data. Then, according to our automatically generated rules, we apply this model in a preprocessing step to reorder the morphemes to better match English sentence structure. After retraining our statistical translation system, Moses, results indicate an improvement in overall translation quality. When the SMT system's internal lexicalized reordering is restricted, we note an increase in the BLEU score of 3.5% over the SMT-only baseline. In the case where we do not limit decoding-time reordering, an even greater BLEU score increase of 4.42% is observed. We also find evidence to suggest that our changes enable Moses to execute additional reordering operations at decoding time that it was previously unable to perform.Chapter 1. Introduction 1 Chapter 2. Literature Review 6 2.1 Machine Translation. 6 2.2 Reordering 10 2.3 Korean to English MT. 12 Chapter 3. Corpus Data and SMT System. 14 3.1 Background 14 3.2 Preparation. 15 3.3 Moses 17 Chapter 4. Rule Generation. 19 4.1 Corpus Processing. 20 4.1.1 Suggested Korean-English Alignments. 21 4.1.2 Feature Sets 24 4.1.3 Reordering Movement. 26 4.2 Rule Creation. 33 4.3 Input Preprocessing. 35 4.3.1 Rule Matching. 35 4.3.2 Morpheme Reordering. 38 4.4 Examples 40 Chapter 5. Results 44 Chapter 6. Conclusion. 49 References 51 Appendix A: Rules 55 Abstract in Korean 64Maste

한국어 사전학습모델 구축과 확장 연구: 감정분석을 중심으로

학위논문 (박사) -- 서울대학교 대학원 : 인문대학 언어학과, 2021. 2. 신효필.Recently, as interest in the Bidirectional Encoder Representations from Transformers (BERT) model has increased, many studies have also been actively conducted in Natural Language Processing based on the model. Such sentence-level contextualized embedding models are generally known to capture and model lexical, syntactic, and semantic information in sentences during training. Therefore, such models, including ELMo, GPT, and BERT, function as a universal model that can impressively perform a wide range of NLP tasks. This study proposes a monolingual BERT model trained based on Korean texts. The first released BERT model that can handle the Korean language was Google Research’s multilingual BERT (M-BERT), which was constructed with training data and a vocabulary composed of 104 languages, including Korean and English, and can handle the text of any language contained in the single model. However, despite the advantages of multilingualism, this model does not fully reflect each language’s characteristics, so that its text processing performance in each language is lower than that of a monolingual model. While mitigating those shortcomings, we built monolingual models using the training data and a vocabulary organized to better capture Korean texts’ linguistic knowledge. Therefore, in this study, a model named KR-BERT was built using training data composed of Korean Wikipedia text and news articles, and was released through GitHub so that it could be used for processing Korean texts. Additionally, we trained a KR-BERT-MEDIUM model based on expanded data by adding comments and legal texts to the training data of KR-BERT. Each model used a list of tokens composed mainly of Hangul characters as its vocabulary, organized using WordPiece algorithms based on the corresponding training data. These models reported competent performances in various Korean NLP tasks such as Named Entity Recognition, Question Answering, Semantic Textual Similarity, and Sentiment Analysis. In addition, we added sentiment features to the BERT model to specialize it to better function in sentiment analysis. We constructed a sentiment-combined model including sentiment features, where the features consist of polarity and intensity values assigned to each token in the training data corresponding to that of Korean Sentiment Analysis Corpus (KOSAC). The sentiment features assigned to each token compose polarity and intensity embeddings and are infused to the basic BERT input embeddings. The sentiment-combined model is constructed by training the BERT model with these embeddings. We trained a model named KR-BERT-KOSAC that contains sentiment features while maintaining the same training data, vocabulary, and model configurations as KR-BERT and distributed it through GitHub. Then we analyzed the effects of using sentiment features in comparison to KR-BERT by observing their performance in language modeling during the training process and sentiment analysis tasks. Additionally, we determined how much each of the polarity and intensity features contributes to improving the model performance by separately organizing a model that utilizes each of the features, respectively. We obtained some increase in language modeling and sentiment analysis performances by using both the sentiment features, compared to other models with different feature composition. Here, we included the problems of binary positivity classification of movie reviews and hate speech detection on offensive comments as the sentiment analysis tasks. On the other hand, training these embedding models requires a lot of training time and hardware resources. Therefore, this study proposes a simple model fusing method that requires relatively little time. We trained a smaller-scaled sentiment-combined model consisting of a smaller number of encoder layers and attention heads and smaller hidden sizes for a few steps, combining it with an existing pre-trained BERT model. Since those pre-trained models are expected to function universally to handle various NLP problems based on good language modeling, this combination will allow two models with different advantages to interact and have better text processing capabilities. In this study, experiments on sentiment analysis problems have confirmed that combining the two models is efficient in training time and usage of hardware resources, while it can produce more accurate predictions than single models that do not include sentiment features.최근 트랜스포머 양방향 인코더 표현 (Bidirectional Encoder Representations from Transformers, BERT) 모델에 대한 관심이 높아지면서 자연어처리 분야에서 이에 기반한 연구 역시 활발히 이루어지고 있다. 이러한 문장 단위의 임베딩을 위한 모델들은 보통 학습 과정에서 문장 내 어휘, 통사, 의미 정보를 포착하여 모델링한다고 알려져 있다. 따라서 ELMo, GPT, BERT 등은 그 자체가 다양한 자연어처리 문제를 해결할 수 있는 보편적인 모델로서 기능한다. 본 연구는 한국어 자료로 학습한 단일 언어 BERT 모델을 제안한다. 가장 먼저 공개된 한국어를 다룰 수 있는 BERT 모델은 Google Research의 multilingual BERT (M-BERT)였다. 이는 한국어와 영어를 포함하여 104개 언어로 구성된 학습 데이터와 어휘 목록을 가지고 학습한 모델이며, 모델 하나로 포함된 모든 언어의 텍스트를 처리할 수 있다. 그러나 이는 그 다중언어성이 갖는 장점에도 불구하고, 각 언어의 특성을 충분히 반영하지 못하여 단일 언어 모델보다 각 언어의 텍스트 처리 성능이 낮다는 단점을 보인다. 본 연구는 그러한 단점들을 완화하면서 텍스트에 포함되어 있는 언어 정보를 보다 잘 포착할 수 있도록 구성된 데이터와 어휘 목록을 이용하여 모델을 구축하고자 하였다. 따라서 본 연구에서는 한국어 Wikipedia 텍스트와 뉴스 기사로 구성된 데이터를 이용하여 KR-BERT 모델을 구현하고, 이를 GitHub을 통해 공개하여 한국어 정보처리를 위해 사용될 수 있도록 하였다. 또한 해당 학습 데이터에 댓글 데이터와 법조문과 판결문을 덧붙여 확장한 텍스트에 기반해서 다시 KR-BERT-MEDIUM 모델을 학습하였다. 이 모델은 해당 학습 데이터로부터 WordPiece 알고리즘을 이용해 구성한 한글 중심의 토큰 목록을 사전으로 이용하였다. 이들 모델은 개체명 인식, 질의응답, 문장 유사도 판단, 감정 분석 등의 다양한 한국어 자연어처리 문제에 적용되어 우수한 성능을 보고했다. 또한 본 연구에서는 BERT 모델에 감정 자질을 추가하여 그것이 감정 분석에 특화된 모델로서 확장된 기능을 하도록 하였다. 감정 자질을 포함하여 별도의 임베딩 모델을 학습시켰는데, 이때 감정 자질은 문장 내의 각 토큰에 한국어 감정 분석 코퍼스 (KOSAC)에 대응하는 감정 극성(polarity)과 강도(intensity) 값을 부여한 것이다. 각 토큰에 부여된 자질은 그 자체로 극성 임베딩과 강도 임베딩을 구성하고, BERT가 기본으로 하는 토큰 임베딩에 더해진다. 이렇게 만들어진 임베딩을 학습한 것이 감정 자질 모델(sentiment-combined model)이 된다. KR-BERT와 같은 학습 데이터와 모델 구성을 유지하면서 감정 자질을 결합한 모델인 KR-BERT-KOSAC를 구현하고, 이를 GitHub을 통해 배포하였다. 또한 그로부터 학습 과정 내 언어 모델링과 감정 분석 과제에서의 성능을 얻은 뒤 KR-BERT와 비교하여 감정 자질 추가의 효과를 살펴보았다. 또한 감정 자질 중 극성과 강도 값을 각각 적용한 모델을 별도 구성하여 각 자질이 모델 성능 향상에 얼마나 기여하는지도 확인하였다. 이를 통해 두 가지 감정 자질을 모두 추가한 경우에, 그렇지 않은 다른 모델들에 비하여 언어 모델링이나 감정 분석 문제에서 성능이 어느 정도 향상되는 것을 관찰할 수 있었다. 이때 감정 분석 문제로는 영화평의 긍부정 여부 분류와 댓글의 악플 여부 분류를 포함하였다. 그런데 위와 같은 임베딩 모델을 사전학습하는 것은 많은 시간과 하드웨어 등의 자원을 요구한다. 따라서 본 연구에서는 비교적 적은 시간과 자원을 사용하는 간단한 모델 결합 방법을 제시한다. 적은 수의 인코더 레이어, 어텐션 헤드, 적은 임베딩 차원 수로 구성한 감정 자질 모델을 적은 스텝 수까지만 학습하고, 이를 기존에 큰 규모로 사전학습되어 있는 임베딩 모델과 결합한다. 기존의 사전학습모델에는 충분한 언어 모델링을 통해 다양한 언어 처리 문제를 처리할 수 있는 보편적인 기능이 기대되므로, 이러한 결합은 서로 다른 장점을 갖는 두 모델이 상호작용하여 더 우수한 자연어처리 능력을 갖도록 할 것이다. 본 연구에서는 감정 분석 문제들에 대한 실험을 통해 두 가지 모델의 결합이 학습 시간에 있어 효율적이면서도, 감정 자질을 더하지 않은 모델보다 더 정확한 예측을 할 수 있다는 것을 확인하였다.1 Introduction 1 1.1 Objectives 3 1.2 Contribution 9 1.3 Dissertation Structure 10 2 Related Work 13 2.1 Language Modeling and the Attention Mechanism 13 2.2 BERT-based Models 16 2.2.1 BERT and Variation Models 16 2.2.2 Korean-Specific BERT Models 19 2.2.3 Task-Specific BERT Models 22 2.3 Sentiment Analysis 24 2.4 Chapter Summary 30 3 BERT Architecture and Evaluations 33 3.1 Bidirectional Encoder Representations from Transformers (BERT) 33 3.1.1 Transformers and the Multi-Head Self-Attention Mechanism 34 3.1.2 Tokenization and Embeddings of BERT 39 3.1.3 Training and Fine-Tuning BERT 42 3.2 Evaluation of BERT 47 3.2.1 NLP Tasks 47 3.2.2 Metrics 50 3.3 Chapter Summary 52 4 Pre-Training of Korean BERT-based Model 55 4.1 The Need for a Korean Monolingual Model 55 4.2 Pre-Training Korean-specific BERT Model 58 4.3 Chapter Summary 70 5 Performances of Korean-Specific BERT Models 71 5.1 Task Datasets 71 5.1.1 Named Entity Recognition 71 5.1.2 Question Answering 73 5.1.3 Natural Language Inference 74 5.1.4 Semantic Textual Similarity 78 5.1.5 Sentiment Analysis 80 5.2 Experiments 81 5.2.1 Experiment Details 81 5.2.2 Task Results 83 5.3 Chapter Summary 89 6 An Extended Study to Sentiment Analysis 91 6.1 Sentiment Features 91 6.1.1 Sources of Sentiment Features 91 6.1.2 Assigning Prior Sentiment Values 94 6.2 Composition of Sentiment Embeddings 103 6.3 Training the Sentiment-Combined Model 109 6.4 Effect of Sentiment Features 113 6.5 Chapter Summary 121 7 Combining Two BERT Models 123 7.1 External Fusing Method 123 7.2 Experiments and Results 130 7.3 Chapter Summary 135 8 Conclusion 137 8.1 Summary of Contribution and Results 138 8.1.1 Construction of Korean Pre-trained BERT Models 138 8.1.2 Construction of a Sentiment-Combined Model 138 8.1.3 External Fusing of Two Pre-Trained Models to Gain Performance and Cost Advantages 139 8.2 Future Directions and Open Problems 140 8.2.1 More Training of KR-BERT-MEDIUM for Convergence of Performance 140 8.2.2 Observation of Changes Depending on the Domain of Training Data 141 8.2.3 Overlap of Sentiment Features with Linguistic Knowledge that BERT Learns 142 8.2.4 The Specific Process of Sentiment Features Helping the Language Modeling of BERT is Unknown 143 Bibliography 145 Appendices 157 A. Python Sources 157 A.1 Construction of Polarity and Intensity Embeddings 157 A.2 External Fusing of Different Pre-Trained Models 158 B. Examples of Experiment Outputs 162 C. Model Releases through GitHub 165Docto

A Deterministic Dependency Parser for Japanese

We present a rule-based, deterministic dependency parser for Japanese. It was implemented in C ++, using object classes that reflect linguistic concepts and thus facilitate the transfer of linguistic intuitions into code. The parser first chunks morphemes into one-word phrases and then parses from the right to the left. The average parsing accuracy is 83.6%

Wide-coverage parsing for Turkish

Wide-coverage parsing is an area that attracts much attention in natural language processing research. This is due to the fact that it is the first step tomany other applications in natural language understanding, such as question answering. Supervised learning using human-labelled data is currently the best performing method. Therefore, there is great demand for annotated data. However, human annotation is very expensive and always, the amount of annotated data is much less than is needed to train well-performing parsers. This is the motivation behind making the best use of data available. Turkish presents a challenge both because syntactically annotated Turkish data is relatively small and Turkish is highly agglutinative, hence unusually sparse at the whole word level. METU-Sabancı Treebank is a dependency treebank of 5620 sentences with surface dependency relations and morphological analyses for words. We show that including even the crudest forms of morphological information extracted from the data boosts the performance of both generative and discriminative parsers, contrary to received opinion concerning English. We induce word-based and morpheme-based CCG grammars from Turkish dependency treebank. We use these grammars to train a state-of-the-art CCG parser that predicts long-distance dependencies in addition to the ones that other parsers are capable of predicting. We also use the correct CCG categories as simple features in a graph-based dependency parser and show that this improves the parsing results. We show that a morpheme-based CCG lexicon for Turkish is able to solve many problems such as conflicts of semantic scope, recovering long-range dependencies, and obtaining smoother statistics from the models. CCG handles linguistic phenomena i.e. local and long-range dependencies more naturally and effectively than other linguistic theories while potentially supporting semantic interpretation in parallel. Using morphological information and a morpheme-cluster based lexicon improve the performance both quantitatively and qualitatively for Turkish. We also provide an improved version of the treebank which will be released by kind permission of METU and Sabancı