Jessica Ficler

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Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic postprocessing or to information loss. To directly address the(More)
Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning(More)
The problem of partitioning an edge-capacitated graph on n vertices into k balanced parts has been amply researched. Motivated by applications such as load balancing in distributed systems and market segmentation in social networks, we propose a new variant of the problem, called Multiply Balanced k Partitioning, where the vertex-partition must be balanced(More)
We propose an intermediary-level semantic representation, providing a higher level of abstraction than syntactic parse trees, while not committing to decisions in cases such as quantification, grounding or verbspecific roles assignments. The proposal is centered around the proposition structure of the text, and includes also implicit propositions which can(More)
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