Collocation google
In corpus linguistics, a collocation is a sequence of words or terms that co-occur more often than would be expected by chance. In phraseology, collocation is a sub-type of phraseme. An example of a phraseological collocation, as propounded by Michael Halliday, is the expression strong tea. While the same meaning could be conveyed by the roughly equivalent *powerful tea, this expression is considered incorrect by English speakers. Conversely, the corresponding expression for computer, powerful computers is preferred over *strong computers. Phraseological collocations should not be confused with idioms, where meaning is derived, whereas collocations are mostly compositional. There are about six main types of collocations: adjective+noun, noun+noun (such as collective nouns), verb+noun, adverb+adjective, verbs+prepositional phrase (phrasal verbs), and verb+adverb. Collocation extraction is a task that extracts collocations automatically from a corpus, using computational linguistics. …

Hybrid Ant Colony Optimization Algorithm (HACO) google
In this paper, we propose a Hybrid Ant Colony Optimization algorithm (HACO) for Next Release Problem (NRP). NRP, a NP-hard problem in requirement engineering, is to balance customer requests, resource constraints, and requirement dependencies by requirement selection. Inspired by the successes of Ant Colony Optimization algorithms (ACO) for solving NP-hard problems, we design our HACO to approximately solve NRP. Similar to traditional ACO algorithms, multiple artificial ants are employed to construct new solutions. During the solution construction phase, both pheromone trails and neighborhood information will be taken to determine the choices of every ant. In addition, a local search (first found hill climbing) is incorporated into HACO to improve the solution quality. Extensively wide experiments on typical NRP test instances show that HACO outperforms the existing algorithms (GRASP and simulated annealing) in terms of both solution uality and running time. …

Expert Iteration google
Solving sequential decision making problems, such as text parsing, robotic control, and game playing, requires a combination of planning policies and generalisation of those plans. In this paper, we present Expert Iteration, a novel algorithm which decomposes the problem into separate planning and generalisation tasks. Planning new policies is performed by tree search, while a deep neural network generalises those plans. In contrast, standard Deep Reinforcement Learning algorithms rely on a neural network not only to generalise plans, but to discover them too. We show that our method substantially outperforms Policy Gradients in the board game Hex, winning 84.4% of games against it when trained for equal time. …

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