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.

Input: Consider Input: In this paper, we propose the distributed tree kernels (DTK) as a novel method to reduce time and space complexity of tree kernels. Using a linear complexity algorithm to compute vectors for trees, we embed feature spaces of tree fragments in low-dimensional spaces where the kernel computation is directly done with dot product. We show that DTKs are faster, correlate with tree kernels, and obtain a statistically similar performance in two natural language processing tasks.

Output: Distributed Tree Kernels


Input: Consider Input: Recently, sequence-to-sequence model by using encoder-decoder neural network has gained popularity for automatic speech recognition (ASR). The architecture commonly uses an attentional mechanism which allows the model to learn alignments between source speech sequence and target text sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in speech recognition task. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results demonstrate that encoder-decoder based ASR with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.

Output: Local Monotonic Attention Mechanism for End-to-End Speech Recognition


Input: Consider Input: We used MetaMap and YTEX as a basis for the construction of two separate systems to participate in the 2013 ShARe/CLEF eHealth Task 1[9], the recognition of clinical concepts. No modifications were directly made to these systems, but output concepts were filtered using stop concepts, stop concept text and UMLS semantic type. Concept boundaries were also adjusted using a small collection of rules to increase precision on the strict task. Overall MetaMap had better performance than YTEX on the strict task, primarily due to a 20% performance improvement in precision. In the relaxed task YTEX had better performance in both precision and recall giving it an overall F-Score 4.6% higher than MetaMap on the test data. Our results also indicated a 1.3% higher accuracy for YTEX in UMLS CUI mapping.


Output: Evaluation of YTEX and MetaMap for clinical concept recognition