SLAM

Syntactic parsing assigns sentence structure in NLP, enabling machines to understand and process human language for improved text analysis. Syntactic parsing can be broadly categorized into two methods: constituency parsing and dependency parsing. Constituency parsing focuses on syntactic analysis, while dependency parsing can handle both syntactic and semantic analysis. Recent research has explored various aspects of syntactic parsing, such as the effectiveness of different parsing methods, the role of syntax in the brain, and the application of parsing techniques in text-to-speech systems. One study investigated the predictive power of constituency and dependency parsing methods in brain activity prediction, finding that constituency parsers were more effective in certain brain regions, while dependency parsers were better in others. Another research paper proposed a new method called SSUD (Syntactic Substitutability as Unsupervised Dependency Syntax) to induce syntactic structures without supervision from gold-standard parses, demonstrating quantitative and qualitative gains on dependency parsing tasks. In the field of text-to-speech, a syntactic representation learning method based on syntactic parse tree traversal was proposed to automatically utilize syntactic structure information, resulting in improved prosody and naturalness of synthesized speech. Additionally, a comparison of popular syntactic parsers on biomedical texts was conducted to evaluate their performance in the context of biomedical text mining. Practical applications of syntactic parsing include: 1. Text-to-speech systems: Incorporating syntactic structure information can improve the prosody and naturalness of synthesized speech. 2. Information extraction: Syntactic parsing can enhance the recall and precision of text mining results, particularly in specialized domains like biomedical texts. 3. Machine translation: Integrating source syntax into neural machine translation can lead to improved translation quality, as demonstrated by a multi-source syntactic neural machine translation model. A company case study in this area is Google, which has developed the Google Syntactic Ngrams corpus, a collection of subtree counts of parsed sentences from scanned books. This corpus has been used to develop novel first- and second-order features for dependency parsing, resulting in substantial and complementary gains in parsing accuracy across domains. In conclusion, syntactic parsing is a vital component of natural language processing, with numerous practical applications and ongoing research exploring its potential. As our understanding of syntactic parsing continues to grow, we can expect further advancements in the field, leading to more sophisticated and effective language processing systems.

Synthetic Minority Over-sampling Technique (SMOTE) helps fix class imbalance in machine learning by generating synthetic samples for the minority class. Recent research has explored various modifications and extensions of SMOTE to further enhance its effectiveness. SMOTE-ENC, for example, encodes nominal features as numeric values and can be applied to both mixed datasets and nominal-only datasets. Deep SMOTE adapts the SMOTE idea in deep learning architecture, using a deep neural network regression model to train the inputs and outputs of traditional SMOTE. LoRAS, another oversampling approach, employs Localized Random Affine Shadowsampling to oversample from an approximated data manifold of the minority class, resulting in better ML models in terms of F1-Score and Balanced accuracy. Generative Adversarial Network (GAN)-based approaches, such as GBO and SSG, have also been proposed to overcome the limitations of existing oversampling methods. These techniques leverage GAN's ability to create almost real samples, improving the performance of machine learning models on imbalanced datasets. Other methods, like GMOTE, use Gaussian Mixture Models to generate instances and adapt tail probability of outliers, demonstrating robust performance when combined with classification algorithms. Practical applications of SMOTE and its variants can be found in various domains, such as healthcare, finance, and cybersecurity. For instance, SMOTE has been used to generate instances of the minority class in an imbalanced Coronary Artery Disease dataset, improving the performance of classifiers like Artificial Neural Networks, Decision Trees, and Support Vector Machines. In another example, SMOTE has been employed in privacy-preserving integrated analysis across multiple institutions, improving recognition performance and essential feature selection. In conclusion, SMOTE and its extensions play a crucial role in addressing class imbalance in machine learning, leading to improved model performance and more accurate predictions. As research continues to explore novel modifications and applications of SMOTE, its impact on the field of machine learning is expected to grow, benefiting a wide range of industries and applications.