[PDF] Top 20 CIST System for CL SciSumm 2016 Shared Task

... After more detailed analysis of the results, we can find two major problems. One is choosing too much sentences, in which the citation text in standard answer contains only one fitful sentence, but we chose more ... See full document

... We trained 6 single classifiers based on different combinations of vector space features, text granu- larities and label sets. We also explored ensemble classifiers (based on these 6 single classifiers), as this is a way ... See full document

... Support Vector Machines (SVM): we experimented with SVMs and found that it produces worse re- sults in comparison to other classifiers. As a result, we did not submit a run that implements SVM. Logistic Regression (LR) ... See full document

... this task, we carried out a manual anal- ysis and classification of the types of insertion and deletion editing operations annotated in the train- ing and development ...the system on sample data sets drawn ... See full document

... that system performance correlates posi- tively with the quality of name conversion across languages (Demner-Fushman and Oard 2002, Mandl and Womser-Hacker 2005,Hermjakobet ... See full document

... a shared task participant does not have to re-implement the model from ...ing system is deployed, and then implement their extension based off the original source ...the system is based off ... See full document

... parsing system for our participation in the CoNLL 2016 Shared ...plementary task: Sense Classification, es- pecially the Non-Explicit one which is the bottleneck of discourse parsing ...best ... See full document

... Martin Potthast, Tim Gollub, Francisco Rangel, Paolo Rosso, Efstathios Stamatatos, and Benno Stein. 2014. Improving the Reproducibility of PAN’s Shared Tasks: Plagiarism Detection, Author Iden- tification, and ... See full document

... Next, candidate sentence pairs for non-explicit relations are identified. The non-explicit sense classifier is applied to these sentence pairs. At this stage, it is run with the goal of separating En- tRel relations from ... See full document

... Topic models: Using the gensim topic modelling library ( ˇReh˚uˇrek and Sojka, 2010) we trained sev- eral LDA (Blei et al., 2003) and LSI (Deerwester et al., 1990) topic models with varying granular- ity (k = 20, 50, ... See full document

... participant system results are computed through the comparison of predicted events against reference corpus ...This task can be viewed as a classification task of all pairs of ... See full document

... composite task of detecting ex- plicit and non-explicit discourse relations, their connective and argument spans, and assigning a sense to these ...the task, the end-to-end performance is greatly affected ... See full document

... APE system fur- ther incorporates a quality estimation (QE) model, which aims to select the best trans- lation between the MT output and the automatic ...the shared task results, our primary and con- ... See full document

... ments a simple linear classifier based on LibSVM with its L2-loss SVC dual solver. The system applies local source-language and target-language context using the given tokens and PoS labels as features. ... See full document

... We present our approach to predicting the severity of user posts in a mental health forum. This system was developed to compete in the 2016 Computational Linguistics and Clinical Psychology (CLPsych) ... See full document

... 6.3 Camp 3: Time for Some Linguistics The third camp relied on linguistics-inspired heuristics to reduce the problem to multi-way clas- sification. This camp is less unified than the other two, as both teams used very ... See full document

... As shown in Figure 2 and Figure 3, the system misclassified all Arabic varieties with each other with different confusion degrees. Gulf Arabic is the most variety for which most mistakes are made, while MSA is the ... See full document

... Previous systems for relation and event extrac- tion have taken two main approaches: rule-based and feature-based. Rule-based methods learn spe- cific patterns that fit different events, for instance, the word ... See full document

... From Table 4, we observe that the mental dis- ease lexicon feature set was the one capable of capturing the single instance of crisis in the test data; additionally, it improved the recall of red and precision of amber. ... See full document

... The most similar work to ours was probably the one by Faruqui et al. (2015). Indeed, MED’s design is very close to their model. However, they trained one network for every tag pair; this can negatively impact performance ... See full document