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
Collaborative filtering or recommender sys tems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, in cluding techniques based on correlation coef ficients, vector-based similarity calculations, and statistical Bayesian methods. We com pare the predictive accuracy of the various methods in a set of representative problem domains. We use two basic classes of evalua tion metrics. The first characterizes accuracy over a set of individual predictions in terms of average absolute deviation. The second esti mates the utility of a ranked list of suggested items. This metric uses an estimate of the probability that a user will see a recommen dation in an ordered list. Experiments were run for datasets associ ated with 3 application areas, 4 experimen tal protocols, and the 2 evaluation met rics for the various algorithms. Results indicate that for a wide range of con ditions, Bayesian networks with decision trees at each node and correlation methods outperform Bayesian-clustering and vector similarity methods. Between correlation and Bayesian networks, the preferred method de pends on the nature of the dataset, nature of the application (ranked versus one-by-one presentation), and the availability of votes with which to make predictions. Other con siderations include the size of database, speed of predictions, and learning time.
Answer:
Empirical Analysis of Predictive Algorithms for Collaborative Filtering