Text classification is a popular and well-studied problem in Natural Language Processing. Most previous work on text classification has focused on deep neural networks such as LSTMs and CNNs. However, text classification studies using syntactic and semantic information are very limited in the literature. In this study, we propose a model using Graph Attention Network (GAT) that incorporates semantic and syntactic information as input for the text classification task. The semantic representations of UCCA and AMR are used as semantic information and the dependency tree is used as syntactic information. Extensive experimental results and in-depth analysis show that UCCA-GAT model, which is a semantic-aware model outperforms the AMR-GAT and DEP-GAT, which are semantic and syntax-aware models respectively. We also provide a comprehensive analysis of the proposed model to understand the limitations of the representations for the problem.
We introduce a neural Turkish NLP toolkit called TurkishDelightNLP that performs computational linguistic analyses from morphological level to semantic level that involves tasks such as stemming, morphological segmentation, morphological tagging, part-of-speech tagging, dependency parsing, and semantic parsing, as well as high-level NLP tasks such as named entity recognition. We publicly share the open-source Turkish NLP toolkit through a web interface that allows an input text to be analysed in real-time, as well as the open source implementation of the components provided in the toolkit, an API, and several annotated datasets such as word similarity test set to evaluate word embeddings and UCCA-based semantic annotation in Turkish. This will be the first open-source Turkish NLP toolkit that involves a range of NLP tasks in all levels. We believe that it will be useful for other researchers in Turkish NLP and will be also beneficial for other high-level NLP tasks in Turkish.
Universal Conceptual Cognitive Annotation (UCCA) (Abend and Rappoport, 2013a) is a cross-lingual semantic annotation framework that provides an easy annotation without any requirement for linguistic background. UCCA-annotated datasets have been already released in English, French, and German. In this paper, we introduce the first UCCA-annotated Turkish dataset that currently involves 50 sentences obtained from the METU-Sabanci Turkish Treebank (Atalay et al., 2003; Oflazeret al., 2003). We followed a semi-automatic annotation approach, where an external semantic parser is utilised for an initial annotation of the dataset, which is partially accurate and requires refinement. We manually revised the annotations obtained from the semantic parser that are not in line with the UCCA rules that we defined for Turkish. We used the same external semantic parser for evaluation purposes and conducted experiments with both zero-shot and few-shot learning. While the parser cannot predict remote edges in zero-shot setting, using even a small subset of training data in few-shot setting increased the overall F-1 score including the remote edges. This is the initial version of the annotated dataset and we are currently extending the dataset. We will release the current Turkish UCCA annotation guideline along with the annotated dataset.
Term and glossary management are vital steps of preparation of every language specialist, and they play a very important role at the stage of education of translation professionals. The growing trend of efficient time management and constant time constraints we may observe in every job sector increases the necessity of the automatic glossary compilation. Many well-performing bilingual AET systems are based on processing parallel data, however, such parallel corpora are not always available for a specific domain or a language pair. Domain-specific, bilingual access to information and its retrieval based on comparable corpora is a very promising area of research that requires a detailed analysis of both available data sources and the possible extraction techniques. This work focuses on domain-specific automatic terminology extraction from comparable corpora for the English – Russian language pair by utilizing neural word embeddings.
We propose a novel deep neural architecture for dependency parsing, which is built upon a Transformer Encoder (Vaswani et al. 2017) and a Stack Pointer Network (Ma et al. 2018). We first encode each sentence using a Transformer Network and then the dependency graph is generated by a Stack Pointer Network by selecting the head of each word in the sentence through a head selection process. We evaluate our model on Turkish and English treebanks. The results show that our trasformer-based model learns long term dependencies efficiently compared to sequential models such as recurrent neural networks. Our self attended stack pointer network improves UAS score around 6% upon the LSTM based stack pointer (Ma et al. 2018) for Turkish sentences with a length of more than 20 words.
This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process. Tree hierarchies are learned along with the corresponding morphological paradigms simultaneously. Our model is evaluated on Morpho Challenge and shows competitive performance when compared to state-of-the-art unsupervised morphological segmentation systems. Although we apply this model for morphological segmentation, the model itself can also be used for hierarchical clustering of other types of data.
In this paper, we investigate the effects of using subword information in representation learning. We argue that using syntactic subword units effects the quality of the word representations positively. We introduce a morpheme-based model and compare it against to word-based, character-based, and character n-gram level models. Our model takes a list of candidate segmentations of a word and learns the representation of the word based on different segmentations that are weighted by an attention mechanism. We performed experiments on Turkish as a morphologically rich language and English with a comparably poorer morphology. The results show that morpheme-based models are better at learning word representations of morphologically complex languages compared to character-based and character n-gram level models since the morphemes help to incorporate more syntactic knowledge in learning, that makes morpheme-based models better at syntactic tasks.