This paper is concerned with the discovery and aggregation of events that provoke a particular emotion in the person who experiences them, or emotion-provoking events. We first describe the creation of a small manually-constructed dictionary of events through a survey of 30 subjects. Next, we describe first attempts at automatically acquiring and aggregating these events from web data, with a baseline from previous work and some simple extensions using seed expansion and clustering. Finally, we propose several evaluation measures for evaluating the automatically acquired events, and perform an evaluation of the effectiveness of automatic event extraction.
The WMT Bilingual Document Alignment Task requires systems to assign source pages to their "translations", in a big space of possible pairs. We present four methods: The first one uses the term position similarity between candidate document pairs. The second method requires automatically translated versions of the target text, and matches them with the candidates. The third and fourth methods try to overcome some of the challenges presented by the nature of the corpus, by considering the string similarity of source URL and candidate URL, and combining the first two approaches.
We present in this paper our team LCT-MALTA's submission to the RepEval 2017 Shared Task on natural language inference. Our system is a simple system based on a standard BiLSTM architecture, using as input GloVe word embeddings augmented with further linguistic information. We use max pooling on the BiLSTM outputs to obtain embeddings for sentences. On both the matched and the mismatched test sets, our system clearly beats the shared task's BiLSTM baseline model.
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