[PDF] Top 20 Learning Representations for Weakly Supervised Natural Language Processing Tasks

... For supervised NLP tasks with sufficient domain-specific training data, these traditional features yield state-of-the-art ...NLP tasks (Daum´e III and Marcu 2006; Chelba and Acero 2004; Downey, ... See full document

... Recent work on generating sentence embed- dings range from models that compose word em- beddings (Le and Mikolov, 2014; Arora et al., 2017; Wieting et al., 2016b) to more complex neu- ral network architectures. ... See full document

... Natural language data is implicitly richly struc- tured, and making use of that structure can be valuable in a wide variety of NLP ...unsupervised learning of (latent) structure prediction with a ... See full document

... machine learning of lan- ...the learning algorithm can also be represented as genes ...these representations, we can make the GA search for an optimal combination of parameter settings and feature ... See full document

... of supervised machine learning approaches for English NLP ...machine learning approaches have shown remarkable generaliza- tion ...One natural step is to apply these approaches to NLP ... See full document

... of natural language processing, mor- phological segmentation is a well-researched and established problem (Goldsmith (2001), Creutz and Lagus (2005), Poon et ...space representations of words ... See full document

... vised learning for NLP is better performed in- context instead of against a labeled gold standard leads to the use of more appropriate experimen- tal ...unsupervised learning meth- ods are restricted to ... See full document

... semi-supervised learning only be employed in low-resource languages/tasks ...semi-supervised learning to improve on a supervised system that is already more than 95% ... See full document

... complex tasks such as booking a movie ticket have become an emerging research topic in artificial in- telligence and natural language processing ...certain tasks more easily via ... See full document

... final language model is a novel latent-variable language model with rich latent structure, shown in Figure ...a supervised setting rather than for representation ... See full document

... Multi-view weakly supervised learning paradigms such as co-training (Blum and Mitchell, 1998) and co-EM (Nigam and Ghani, 2000) learn a classification task from a small set of labeled data and a ... See full document

... Two main types of methods have been proposed to compensate for a lack of training data. One type is the semi-supervised learning method (Niu et al., 2005; Pham et al., 2005) or bootstrapping (Mihal- cea, ... See full document

... two learning challenges: grammar in- duction, which assigns meaning representations to words and phrases, and parameter estimation, where we learn a model for combining these pieces to analyze full ... See full document

... both tasks, we define the space of possible compositional mean- ing representations Z, where each z ∈ Z defines a unique time expression ...For learning we assume access to TimeML data containing ... See full document

... Apart from the entity score, the discriminative ranker uses the following basic features. The first feature is the likelihood score of a grounded rep- resentation aggregating all intermediate represen- tations. The ... See full document

... One widely used approach for that is Multiple Ker- nel Learning (MKL) (G¨onen and Alpaydın, 2011). MKL is based on the idea of using combinations of kernels to model the data and developing algo- rithms to tune ... See full document

... many natural language processing (NLP) ...NLP tasks to confirm the prob- lem with a conventional method and to compare its performance with that of the proposed ... See full document

... various tasks (Shang et ...word representations) and dynamically fetches the relevant piece of information based mostly on the feedback from the generation of the output ... See full document

... machine learning algorithms, data sparse- ness combined with noise will likely yield over- fitted models, which means that the constructed tree will model a features/label combination that will never exists in ... See full document

... There are a lot of noisy texts surrounding a person in modern life. A traditional approach is to use spelling correction, yet the existing solutions are far from perfect. We propose a robust to noise word embeddings ... See full document