instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results largely confirm previous findings that character representations are effective across many languages, though we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most other settings. However, we also find room for improvement: character models do not match the predictive accuracy of a model with access to explicit morphological analyses.
answer:
From Characters to Words to in Between: Do We Capture Morphology?

question:
Protein subcellular localization prediction is an important and challenging problem. The traditional biology experiments are expensive and time-consuming, so more and more research interests tend to a series of machine learning approaches for predicting protein subcellular location. There are two main difficult problems among the existing state-of-the-art methods. First, most of the existing techniques are designed to deal with the multi-class but not the multi-label classification, which ignores the connection between the multiple labels. In reality, multiple location proteins implicate that there are vital and unique biological significances worthy of special focus, which cannot be ignored. Second, the techniques for handling imbalanced data in multi-label classification problem is significant but less. For solving the two issues, we have developed an ensemble multi-label classifier called HPSLPred which can be applied for the multi-label classification with imbalanced protein source. For the conveniences of users, a user-friendly webserver for HPSLPred was established at http://server.malab.cn/HPSLPred.
answer:
HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

question:
Whereas acausal Bayesian networks rep resent probabilistic independence, causal Bayesian networks represent causal relation ships. In this paper, we examine Bayesian methods for learning both types of networks. Bayesian methods for learning acausal net works are fairly well developed. These meth ods often employ assumptions to facilitate the construction of priors, including the as sumptions of parameter independence, pa rameter modularity, and likelihood equiva lence. We show that although these assump tions also can be appropriate for learning causal networks, we need additional assump tions in order to learn causal networks. We introduce two sufficient assumptions, called mechanism independence and component in dependence. We show that these new as sumptions, when combined with parame ter independence, parameter modularity, and likelihood equivalence, allow us to apply methods for learning acausal networks to learn causal networks.
answer:
A Bayesian Approach to Learning Causal Networks