Part 1. Definition
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.
Part 2. Example
We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Answer: A Neural Local Coherence Model
Explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage
Part 3. Exercise
A Bayesian Belief Network (BN) is a model of a joint distribution over a finite set of variables, with a DAG structure to represent the immedi ate dependencies between the variables, and a set of parameters (aka CPTables) to represent the local conditional probabilities of a node, given each assignment to its parents. In many situa tions, the parameters are themselves treated as random variablesreflecting the uncertainty re maining after drawing on knowledge of domain experts and/or observing data generated by the network. A distribution over the CPtable param eters induces a distribution for the response the BN will return to any "What is Pr{ H I E} ?" query. This paper investigates the distribution of this response, shows that it is asymptotically normal, and derives expressions for its mean and asymptotic variance. We show that this compu tation has the same complexity as simply com puting the (mean value of the) response -i.e., O(n exp(w)), where n is the number of vari ables and w is the effective tree width. We also provide empirical evidence showing that the error-bars computed from our estimates are fairly accurate in practice, over a wide range of belief net structures and queries.
Answer:
Bayesian Error-Bars for Belief Net Inference