prediction tasks
Residualized Factor Adaptation for Community Social Media Prediction Tasks
... Language-based prediction tasks involving com- munities can benefit from both socio-demographic factors and linguistic ...community-level prediction tasks across three do- ...
10
A functional link neural network with modified cuckoo search for prediction tasks
... acknowledges, prediction of climate change is a subject that is hard to implement because it includes a variation of physical ...for prediction tasks’ accuracy due to the complex environment of the ...
43
Posterior regularization for Joint Modeling of Multiple Structured Prediction Tasks with Soft Constraints
... various tasks like bilingual NER (Che et ...structured prediction models in presence of hard constraints, which incorporate discrete penalty associated with label combinations relevant to the constraint ...
9
Representation learning for clinical time series prediction tasks in electronic health records
... Meanwhile, patient representations are widely used in several applications to assist clinical staff. Considerable efforts were made to learn dense vector representations at the patient level. For example, Zhou et al. ...
14
Stock data models analysis based on window mechanism
... The rest of the paper is organized as follows: Section 2 describes the process of experiment, include the source of data, the define of the Prediction Tasks , the choice of technical indicators, built the ...
6
Distilling Knowledge for Search based Structured Prediction
... processing tasks can be modeled into structured prediction and solved as a search ...structured prediction tasks – transition-based dependency pars- ing and neural machine translation show ...
10
Multi Task Learning for Coherence Modeling
... two prediction tasks and achieve state-of-the-art results in predicting document-level coherence; (2) We assess the ex- tent to which the information encoded in the net- work generalizes to different ...
11
Using lexical semantic knowledge from machine readable dictionaries for domain independent language modelling
... confused with short content words (e.g. “red” with “and”, “Ann” with “an”, etc.) and because there is not enough context to disambiguate them, occasional errors can occur. It should be noted however, that about one third ...
7
Number Sequence Prediction Problems for Evaluating Computational Powers of Neural Networks
... sequence prediction tasks to assess neural network models’ computational powers for solving algorith- mic ...sequence prediction task with the structure of the smallest automaton that can generate ...
8
Referential Translation Machines for Predicting Translation Quality and Related Statistics
... translation prediction tasks and Global Linear Models (GLM) (Collins, 2002) with dynamic learning (GLMd) (Bic¸ici, 2013; Bic¸ici and Way, 2014) for word-level translation per- formance ...
5
Segmentation Free Word Embedding for Unsegmented Languages
... category prediction tasks on raw Twitter, Weibo, and Wikipedia corpora show that the proposed method outper- forms the conventional approaches that re- quire word ...
6
"SHE WILL DRIVE THE _____": VERB-BASED PREDICTION IN INDIVIDUALS WITH PARKINSON DISEASE.
... non-linguistic prediction tasks, suggesting that a prediction deficit ...weather prediction task is one example of a probabilistic learning task in which PwPD are ...explicit prediction ...
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A Multi task Approach to Predict Likability of Books
... related tasks, for example predicting the quality of text from lexical features, syntactic fea- tures and different measures of ...the prediction of great writing in science ...
11
Stance Classification, Outcome Prediction, and Impact Assessment: NLP Tasks for Studying Group Decision Making
... In outcome prediction, we find that text mod- els underperform the gold-labels model when pre- dicting an outcome of Keep, particularly for short debates. As seen in Table 4, when predicting Delete in short ...
13
Multimodal Machine Translation with Embedding Prediction
... Word Embeddings Furthermore, we found that decoder embeddings must be fixed to improve multimodal machine translation with embedding prediction. When we allow fine-tuning on the em- bedding layer, the performance ...
6
Multi Granularity Self Attention for Neural Machine Translation
... Multi Granularity Representation Multi- granularity representation, which is proposed to make full use of subunit composition at different levels of granularity, has been explored in various NLP tasks, such as ...
11
Online Full Text
... of tasks in a run queue, Beltrán et ...of tasks in such a way that the completion time of all the tasks is minimized ...of tasks (each with its own CPU requirements) and their analytical model ...
6
MainiwayAI at IJCNLP 2017 Task 2: Ensembles of Deep Architectures for Valence Arousal Prediction
... This paper introduces Mainiway AI Labs submitted system for the IJCNLP 2017 shared task on Dimensional Sentiment Analysis of Chinese Phrases (DSAP), and related experiments. Our approach con- sists of deep neural ...
6
Learning to Skim Text
... The results of LSTM and our method, LSTM- Jump, are shown in Table 3. The first observa- tion is that LSTM-Jump is faster than LSTM; the longer the sequence is, the more significant speed- up LSTM-Jump can gain. This is ...
11
Sentiment Tagging with Partial Labels using Modular Architectures
... NLP tasks and borrow the notation. In the target-sentiment tasks we address in this paper, the segmentation tagging task can be considered as a “where”-task ...
12