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.

Q: The Complete Tang Poems (CTP) is the most important source to study Tang poems. We look into CTP with computational tools from specific linguistic perspectives, including distributional semantics and collocational analysis. From such quantitative viewpoints, we compare the usage of “wind” and “moon” in the poems of Li Bai (李白) and Du Fu (杜甫). Colors in poems function like sounds in movies, and play a crucial role in the imageries of poems. Thus, words for colors are studied, and “白” (bai2, white) is the main focus because it is the most frequent color in CTP. We also explore some cases of using colored words in antithesis(對仗) pairs that were central for fostering the imageries of the poems. CTP also contains useful historical information, and we extract person names in CTP to study the social networks of the Tang poets. Such information can then be integrated with the China Biographical Database of Harvard University.

A: Color Aesthetics and Social Networks in Complete Tang Poems: Explorations and Discoveries
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Q: Accurate prediction of suitable discourse connectives (however, furthermore, etc.) is a key component of any system aimed at building coherent and fluent discourses from shorter sentences and passages. As an example, a dialog system might assemble a long and informative answer by sampling passages extracted from different documents retrieved from the web. We formulate the task of discourse connective prediction and release a dataset of 2.9M sentence pairs separated by discourse connectives for this task. Then, we evaluate the hardness of the task for human raters, apply a recently proposed decomposable attention (DA) model to this task and observe that the automatic predictor has a higher F1 than human raters (32 vs. 30). Nevertheless, under specific conditions the raters still outperform the DA model, suggesting that there is headroom for future improvements. Finally, we further demonstrate the usefulness of the connectives dataset by showing that it improves implicit discourse relation prediction when used for model pre-training.

A: Automatic Prediction of Discourse Connectives
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Q: Semantic composition is the task of understanding the meaning of text by composing the meanings of the individual words in the text. Semantic decomposition is the task of understanding the meaning of an individual word by decomposing it into various aspects (factors, constituents, components) that are latent in the meaning of the word. We take a distributional approach to semantics, in which a word is represented by a context vector. Much recent work has considered the problem of recognizing compositions and decompositions, but we tackle the more difficult generation problem. For simplicity, we focus on noun-modifier bigrams and noun unigrams. A test for semantic composition is, given context vectors for the noun and modifier in a noun-modifier bigram (red salmon), generate a noun unigram that is synonymous with the given bigram (sockeye). A test for semantic decomposition is, given a context vector for a noun unigram (snifter), generate a noun-modifier bigram that is synonymous with the given unigram (brandy glass). With a vocabulary of about 73,000 unigrams from WordNet, there are 73,000 candidate unigram compositions for a bigram and 5,300,000,000 (73,000 squared) candidate bigram decompositions for a unigram. We generate ranked lists of potential solutions in two passes. A fast unsupervised learning algorithm generates an initial list of candidates and then a slower supervised learning algorithm refines the list. We evaluate the candidate solutions by comparing them to WordNet synonym sets. For decomposition (unigram to bigram), the top 100 most highly ranked bigrams include a WordNet synonym of the given unigram 50.7% of the time. For composition (bigram to unigram), the top 100 most highly ranked unigrams include a WordNet synonym of the given bigram 77.8% of the time.

A:
Semantic Composition and Decomposition: From Recognition to Generation
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