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
The ability to predict which patterns are formed in brain scans when imagining a celery or an airplane, based on how these concepts as words co-occur in texts, suggests that it is possible to model mental representations based on word statistics. Whether counting how frequently nouns and verbs combine in Google search queries, or extracting eigenvectors from matrices made up of Wikipedia lines and Shakespeare plots, these latent semantics approximate the associative links that form concepts. However, cognition is fundamentally intertwined with action; even passively reading verbs has been shown to activate the same motor circuits as when we tap a finger or observe actual movements. If languages evolved by adapting to the brain, sensorimotor constraints linking articulatory gestures with aspects of motion might also be reflected in the statistics of word co-occurrences. To probe this hypothesis 3 20 emotion, face, and hand related verbs known to activate premotor areas in the brain were selected, and latent semantic analysis LSA was applied to create a weighted adjacency matrix. Hierarchically clustering the verbs and modeling their connectivity within a force directed graph, they divide into modules of mouth and hand motion, facial expressions and negative emotions. Transforming the verbs into their constituent phonemes, the corresponding consonant vowel transitions can be represented in an articulatory space defined by tongue height and formant frequencies. Here the vowels appear positioned along a front to back continuum reflecting aspects of size and intensity related to the actions described by the verbs. More forceful verbs combine plosives and sonorants with fricatives characterized by sustained turbulent airflows, while positive and negative emotional expressions tend to incorporate upor downwards shifts in formant frequencies. Suggesting, that articulatory gestures reflect parameters of size and intensity which might be retrieved from the latent semantics of action verbs.
Answer:
When push comes to shove verbs literally shake due to latent semantic parameters of size and intensity