For each language pair, the source dataset (Europarl) is PoS-tagged and parsed using the transition-based version of the MateParser (Bohnet and Nivre, 2012) with a beam of 40, which was trained on the UDT corpus.
Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result
Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```We use BLEU (up to 4-grams) (Papineni et al., 2002) as evaluation metric, which computes the precision of n-grams in generated summaries with gold-standard abstracts as the reference.```
Sample intent: background



Sample text:```The studies on VA are conducted for several languages and actual datasets are acquired for different languages including English [12, 13], German [14], French [15], Spanish [16] and Chinese [17–19].```
Sample intent: method



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result
Sample text:```Our genealogy, based on many alleles but only the first exon of bindin, is not entirely consistent with that of McCartney and Lessios (2004), based on fewer alleles but incorporating the entire molecule.```
Sample intent: result



Sample text:```distances were calculated between all population pairs considering the likely routes of human migration out of Africa, following the criteria set by Ramachandran et al. (2005). The test was performed with the software Passage version 2.```
Sample intent: background



Sample text:```Because previous studies indicated that hexapeptides bind the junction core (29, 32), we tested two junctions, one with an AT-rich core and the other with a GC-rich core (Fig.```
Sample intent: result



Sample text:```The studies on VA are conducted for several languages and actual datasets are acquired for different languages including English [12, 13], German [14], French [15], Spanish [16] and Chinese [17–19].```
Sample intent: method



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```Note that in their pioneering paper Calvet and Fisher (2002) proposed the simplified version of the construction of Mannersalo et al (2002).```
Sample intent: method



Sample text:```For example, Kawato et al. (1990) suggested that movement trajectories can be projected using critical via-points through which the trajectory has to pass at a certain time, rather than in terms of the moment-by-moment dynamics of the implemented movement trajectory. Optimal trajectories can then be learnt by applying local optimising principles for getting from one via-point to the next. In this way, impoverished predictions can be used to monitor rich implementations. In the same way, language users might predict particularly crucial aspects of an utterance, but the aspects that they predict will depend on the circumstances. More generally, Meyer & Hagoort question the value of predicting one’s own utterance. They argue that prediction is useful when it is likely to differ from the actual event. This is the case when predicting another person’s behaviour or when the result of one’s action is uncertain (e.g., moving in a strong wind). But they argue that people are confident about their own speech. Meyer & Hagoort admit that they will tend to be less confident in dialogue, and we agree. But more important, we argue that the behaviour of the production implementer is not fully determined by the production command, because the complex processes involved in production are subject to internal (“neural”) noise or priming (i.e., influences that may not be a result of the production command). Assuming that these sources of noise do not necessarily affect forward modelling as well, predicted speech may differ from actual speech. In addition, prediction is useful even if the behaviour is fully predictable, because it allows the actor to plan future behaviour on the basis of the prediction. In fact, we made such a proposal in relation to the order of heavy and light phrases (see sect. 3.1, target article). Hartsuiker claims that our account incorrectly predicts an early competitor effect in Huettig and Hartsuiker (2010). His claim is based on the assumption that Response/Pickering & Garrod: An integrated theory of language production and comprehension```
Sample intent: method



Sample text:```We use BLEU (up to 4-grams) (Papineni et al., 2002) as evaluation metric, which computes the precision of n-grams in generated summaries with gold-standard abstracts as the reference.```
Sample intent: background



Sample text:```In this study, we used the Knowledge Map Concept Indexer (KMCI) (Denny et al., 2009), a general-purpose NLP engine, to process all paper titles from American Journal of Epidemiology (AJE).```
Sample intent: background



Sample text:```5 concentrations were computed for each participant using a hierarchical spatiotemporal model fit with a pragmatic estimation procedure described elsewhere [30,31].```
Sample intent: background