In this paper, we present a cross-lingual neural coreference resolution system for a lessresourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it.
This paper presents the improvement process of a mention detector for Basque. The system is rule-based and takes into account the characteristics of mentions in Basque. A classification of error types is proposed based on the errors that occur during mention detection. A deep error analysis distinguishing error types and causes is presented and improvements are proposed. At the final stage, the system obtains an F-measure of 74.57% under the Exact Matching protocol and of 80.57% under Lenient Matching. We also show the performance of the mention detector with gold standard data as input, in order to omit errors caused by the previous stages of linguistic processing. In this scenario, we obtain an F-measure of 85.89% with Strict Matching and of 89.06% with Lenient Matching, i.e., a difference of 11.32 and 8.49 percentage points, respectively. Finally, how improvements in mention detection affect coreference resolution is analysed.
This paper presents our contribution to the SemEval-2015 Task 7. The task was subdivided into three subtasks that consisted of automatically identifying the time period when a piece of news was written (1,2) as well as automatically determining whether a specific phrase in a sentence is relevant or not for a given period of time (3). Our system tackles the resolution of all three subtasks. With this purpose in mind multiple approaches are undertaken that use resources such as Wikipedia or Google NGrams. Final results are obtained by combining the output from all approaches. The texts used for the task are written in English and range from the years 1700 to 2000.
This paper presents X-Space, a system that follows the ISO-Space annotation scheme in order to capture spatial information as well as our contribution to the SemEval-2015 task 8 (SpaceEval). Our system is the only participant system that reported results for all three evaluation configurations in SpaceEval.
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