In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Input: Consider Input: Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages. To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach. The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus. Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb. We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus. Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI). First we build word-context unigram frequency count matrix and transform it to point-wise mutual Information matrix. Using this matrix, we computed the cosine distance of mean vector of seed lists and each word in the corpus vocabulary. Based on the threshold value, the top closest words to the mean vector of seed list are added to the lexicon. Then the mean vector of the new sentiment seed list is updated and process is repeated until we get sufficient terms in the lexicon. Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds. Finally, the lexicon generated in corpus based approach is evaluated.


Output: Corpus based Algorithm is developed generate Amharic Sentiment lexicon relying on corpus


Input: Consider Input: A typical experiment to study cognitive function is to train animals to perform tasks, while the researcher records the electrical activity of the animals neurons. The main obstacle faced, when using this type of electrophysiological experiment to uncover the circuit mechanisms underlying complex behaviors, is our incomplete access to relevant circuits in the brain. One promising approach is to model neural circuits using an artificial neural network (ANN), which can provide complete access to the “neural circuits” responsible for a behavior. More recently, reinforcement learning models have been adopted to understand the functions of cortico-basal ganglia circuits as reward-based learning has been found in mammalian brain. In this paper, we propose a Biologically-plausible Actor-Critic with Episodic Memory (B-ACEM) framework to model a prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit, which is verified to capture the behavioral findings from a well-known perceptual decision-making task, i.e., random dots motion discrimination. This B-ACEM framework links neural computation to behaviors, on which we can explore how episodic memory should be considered to govern future decision. Experiments are conducted using different settings of the episodic memory and results show that all patterns of episodic memories can speed up learning. In particular, salient events are prioritized to propagate reward information and guide decisions. Our B-ACEM framework and the built-on experiments give inspirations to both designs for more standard decision-making models in biological system and a more biologically-plausible ANN.

Output: Fast learning via episodic memory verified by a biologically plausible framework for prefrontal cortex-basal ganglia-hippocampus (PFC-BG) circuit


Input: Consider Input: Recent efforts on combining deep models with probabilistic graphical models are promising in providing flexible models that are also easy to interpret. We propose a variational message-passing algorithm for variational inference in such models. We make three contributions. First, we propose structured inference networks that incorporate the structure of the graphical model in the inference network of variational auto-encoders (VAE). Second, we establish conditions under which such inference networks enable fast amortized inference similar to VAE. Finally, we derive a variational message passing algorithm to perform efficient natural-gradient inference while retaining the efficiency of the amortized inference. By simultaneously enabling structured, amortized, and natural-gradient inference for deep structured models, our method simplifies and generalizes existing methods.


Output: We propose a variational message-passing algorithm for models that contain both the deep model and probabilistic graphical model.