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A Multi task Approach to Predict Likability of Books
... rate books. We consider only those books that have been rated by at least 10 peo- ...that books with average rating < ...our books. To our knowledge, we have one of the largest collection ... See full document
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Modeling and Prediction of Online Product Review Helpfulness: A Survey
... Data Given that we recommend user-specific helpfulness prediction, we propose the develop- ment of a gold standard that contains informa- tion that can facilitate the design of user-specific models (e.g., records of who ... See full document
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A survey on raspberry PI GUI kernel remote authentication multitasking & embedded web control
... approach to real-time programming but controls timing properties through deadlines and events rather than time triggers. By doing so, each piece of information is processed exactly once, and the tasks can be ... See full document
5
A Multi task Approach for Named Entity Recognition in Social Media Data
... a multi-task neural net- work that aims at generalizing the underneath rules of emerging NEs in user-generated ...classification task, we employ an auxiliary but related secondary task called ... See full document
6
Lexicon information in neural sentiment analysis: a multi task learning approach
... For English we use the sentiment lexicon com- piled by Hu and Liu (2004), containing 4,783 neg- ative words and 2,006 positive words. The sen- timent lexicon was a bi-product of their task for predicting which ... See full document
12
Learning to Learn and Predict: A Meta Learning Approach for Multi Label Classification
... Many tasks in natural language processing can be viewed as multi-label classification prob- lems. However, most of the existing mod- els are trained with the standard cross-entropy loss function and use a fixed ... See full document
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All in one : multi task learning for rumour verification
... joint multi- task learning ...our approach. At the base of it is a sequential approach, as discussed above, represented by a shared LSTM layer (hard parameter sharing), which is followed by a ... See full document
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A Multi Task Architecture on Relevance based Neural Query Translation
... embedding task (Mikolov et ...learning approach that is suitable for our task. They tried to predict words from the relevance model (Lavrenko and Croft, 2001) computed from a query, which does ... See full document
6
A Multi Task Approach for Disentangling Syntax and Semantics in Sentence Representations
... We predict a parse tree for each sentence in the test set by finding its near- est neighbor in the training set based on the co- sine similarity of the mean vectors for the syntactic ... See full document
12
A Genre Aware Attention Model to Improve the Likability Prediction of Books
... We propose a model that we call Genre-Aware At- tention model (GA), which dynamically weights features coming from different aspects of a book by using genre supervision. We first feed our textual and visual features ... See full document
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A multi task approach to face deblurring
... source task to improve a significant performance in target ...(domains). Multi-task learning is an inductive trans- fer learning method to solve multiple tasks at the same ...model. Multi- ... See full document
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A Soft Approach to Estimate Woody Volume of a Live Tree
... Tree volume is one of the oldest areas of interest and is a crucial task in tree management system. Estimating the woody volume of a live tree is important for economic, scientific purposes and provides a tool to ... See full document
6
A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks
... a multi-task architecture combining four different tasks that have not been explored together to the best of our knowl- ...for multi-task learning, proportional ...of multi-task ... See full document
8
ADAPT Centre Cone Team at IJCNLP 2017 Task 5: A Similarity Based Logistic Regression Approach to Multi choice Question Answering in an Examinations Shared Task
... We describe the work of a team from the ADAPT Centre in Ireland in addressing automatic answer selection for the Multi- choice Question Answering in Examina- tions shared task. The system is based on a ... See full document
6
Targeted treatment in COPD: a multi-system approach for a multi-system disease
... to discuss treatment of various aspects of COPD, including the pathological processes involved, clinical phenotypes and systemic manifestations. It is apparent that there is significant variation in many of these aspects ... See full document
15
Deep Cascade Multi-Task Learning for Slot Filling in Online Shopping Assistant
... a multi-task learning frame- work is working better than directly applying it on Chinese E-commerce ...a multi- task sequence labeling model with novel cascade and resid- ual connections based ... See full document
8
Cross corpus Native Language Identification via Statistical Embedding
... ding approach that considers descriptive statis- tics such as the distribution skewness and kurto- sis (Gini indexes) as well as the moment informa- tion to represent the documents of the three differ- ent classes ... See full document
5
What can we gain from language models for morphological inflection?
... However, language models demonstrate its util- ity even when little training data is available. The results for low subtask (see 2) demonstrate that they are powerful enough to discriminate between correct and incorrect ... See full document
6
A Multimodal Approach to Improve the Performa...
... It is known that fingerprint and face authentication system combined with soft biometrics has faster response time as compare with “fingerprint and face” authentication system. Proposed scheme (as shown in figure 2) ... See full document
7
MalwareTextDB: A Database for Annotated Malware Articles
... this task requires specialized cybersecu- rity domain knowledge from the annotator and the ability to apply such knowledge in a natu- ral language ...this task ex- tremely ... See full document
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