Jesús Tomás

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Current machine translation (MT) systems are still not perfect. In practice, the output from these systems needs to be edited to correct errors. A way of increasing the productivity of the whole translation process (MT plus human work) is to incorporate the human correction activities within the translation process itself, thereby shifting the MT paradigm(More)
Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different(More)
A cloud does not have infinite computational and storage resources in its infrastructure. If it saturates, it will not be able to satisfy new requests for service allocations sent by its customers. Clouds should interrelate through networking protocols in order to provide scalability, efficiency and flexibility by using the services and the computational(More)
Grouping nodes gives better performance to the whole network by diminishing the average network delay and avoiding unnecessary message forwarding and additional overhead. Many routing protocols for ad-hoc and sensor networks have been designed but none of them are based on groups. In this paper, we will start defining group-based topologies, and then we(More)
Obtaining high-quality machine translations is still a long way off. A postediting phase is required to improve the output of a machine translation system. An alternative is the so called computerassisted translation. In this framework, a human translator interacts with the system in order to obtain high-quality translations. A statistical phrase-based(More)
Accurate evaluation of machine translation (MT) is an open problem. A brief survey of the current approach to tackle this problem is presented and a new proposal is introduced. This proposal attempts to measure the percentage of words, which should be modified at the output of an automatic translator in order to obtain a correct translation. To show the(More)
In the field of pattern recognition, the design of an efficient decoding algorithm is critical for statistical machine translation. The most common statistical machine translation decoding algorithms use the concept of partial hypothesis. Typically, a partial hypothesis is composed by a subset of source positions, which indicates the words that have been(More)
The first pattern recognition approaches to machine translation were based on single-word models. However, these models present an important deficiency; they do not take contextual information into account for the translation decision. The phrase-based approach consists in translating a multiword source sequence into a multiword target sequence, instead of(More)