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.
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Question: Chain graphs combine directed and undi rected graphs and their underlying mathe matics combines properties of the two. This paper gives a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and Markov (undirected) networks. Examples of a chain graph are multivariate feed-forward networks, cluster ing with conditional interaction between vari ables, and forms of Bayes classifiers. Chain graphs are then extended using the notation of plates so that samples and data analysis problems can be represented in a graphical model as well. Implications for learning are discussed in the conclusion.

Answer: Chain graphs for learning

Question: Analysing sentiment of tweets is important as it helps to determine the users' opinion. Knowing people's opinion is crucial for several purposes starting from gathering knowledge about customer base, e-governance, campaignings and many more. In this report, we aim to develop a system to detect the sentiment from tweets. We employ several linguistic features along with some other external sources of information to detect the sentiment of a tweet. We show that augmenting the 140 character-long tweet with information harvested from external urls shared in the tweet as well as Social Media features enhances the sentiment prediction accuracy significantly.

Answer: Sentiment Analysis for Twitter : Going Beyond Tweet Text

Question: We present a data-driven approach to the problem of inductive computer program synthesis. Our method learns a probabilistic model for realworld programs from a corpus of existing code. It uses this model during synthesis to automatically infer a posterior distribution over sketches, or syntactic models of the problem to be synthesized. Sketches sampled from this posterior are then used to drive combinatorial synthesis of a program in a high-level programming language. The key technical innovation of our approach embodied in a system called BAYOU is utilizing user-supplied evidence as to the program's desired behavior, along with a Bayesian update, to obtain a posterior distribution over the program's true, latent specification (indicating user intent), which in turn produces a posterior over possible sketches. As we show experimentally, explicitly modeling uncertainty in specification significantly increases the accuracy of the synthesis algorithm. We evaluate BAYOU's ability to synthesize Java and Android methods. We find that using just a few example API sequences to communicate user intent, BAYOU can synthesize complex method bodies, some implementing tasks never encountered during training.

Answer:
Bayesian Sketch Learning for Program Synthesis