Articles

This article presents our team’s partici- pating system at SemEval-2014 Task 3. Using a meta-learning framework, we experiment with traditional knowledge- based metrics, as well as novel corpus- based measures based on deep learning paradigms, paired with varying degrees of context expansion. The framework en- abled us to reach the …...

SentiKLUE is an update of the KLUE po- larity classifier – which achieved good and robust results in SemEval-2013 with a sim- ple feature set – implemented in 48 hours. 1...

This document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This sys- tem maintains a supervised machine learn- ing approach to …...

This paper describes the Post-Editor Z sys- tem submitted to the L2 writing assis- tant task in SemEval-2014. The aim of task is to build a translation assistance system to translate untranslated sentence fragments. This is not unlike the task of post-editing where human translators improve machine-generated translations. Post-Editor Z …...

This paper describes our SeemGo sys- tem for the task of Aspect Based Sen- timent Analysis in SemEval-2014. The subtask of aspect term extraction is cast as a sequence labeling problem modeled with Conditional Random Fields that ob- tains the F-score of 0.683 for Laptops and 0.791 for Restaurants by …...

Being able to quantify the semantic similar- ity between two texts is important for many practical applications. SemantiKLUE com- bines unsupervised and supervised tech- niques into a robust system for measuring semantic similarity. At the core of the sys- tem is a word-to-word alignment of two texts using a maximum …...

This paper describes my approach for parsing robot commands, which was task 6 at SemEval 2014. My solution is to manually create a compact unifica- tion grammar. The grammar is highly am- biguous, and relies heavily on filtering the parse results by checking their consistency with the current world. The …...

We describe the submission of the SAP Research & Innovation team to the Se- mEval 2014 Task 4: Aspect-Based Senti- ment Analysis (ABSA). Our system fol- lows a constrained and supervised ap- proach for aspect term extraction, catego- rization and sentiment classification of on- line reviews and the details are …...

This paper describes our submission to Se- mEval2014 Task 9: Sentiment Analysis in Twitter. Our model is primarily a lexi- con based one, augmented by some pre- processing, including detection of Multi- Word Expressions, negation propagation and hashtag expansion and by the use of pairwise semantic similarity at the tweet …...

The SAIL-GRS system is based on a widely used approach originating from in- formation retrieval and document index- ing, the TF-IDF measure. In this im- plementation for spoken dialogue system grammar induction, rule constituent fre- quency and inverse rule frequency mea- sures are used for estimating lexical and semantic similarity …...

This paper describes the details of our system submitted to the SemEval-2014 shared task about aspect-based sentiment analysis on review texts. We participated in subtask 2 (prediction of the polarity of aspect terms) and 4 (prediction of the polarity of aspect categories). Our ap- proach to determine the sentiment of …...

This paper describes the enhancements made to our GU-MLT-LT system (G¨unther and Furrer, 2013) for the SemEval-2014 re-run of the SemEval-2013 shared task on sentiment analysis in Twitter. The changes include the usage of a Twitter-specific to- kenizer, additional features and sentiment lexica, feature weighting and random sub- space learning. …...

This paper presents our contribution to the SemEval-2014 shared task on Broad- Coverage Semantic Dependency Parsing. We employ a feature-rich linear model, in- cluding scores for first and second-order dependencies (arcs, siblings, grandparents and co-parents). Decoding is performed in a global manner by solving a linear relax- ation with alternating …...

We refined the performance of Co- coa/Peaberry, a linguistically moti- vated system, on extracting disease en- tities from clinical notes in the train- ing and development sets for Task 7. Entities were identified in noun chunks by use of dictionaries, and events (‘The left atrium is dilated’) through our own …...

We present the Potsdam systems that par- ticipated in the semantic dependency pars- ing shared task of SemEval 2014. They are based on linguistically motivated bidi- rectional transformations between graphs and trees and on utilization of syntactic de- pendency parsing. They were entered in both the closed track and the …...

Using the SemEval-2014 Task 8 data, we profile the syntactic tree parsing tech- niques for semantic graph parsing. In par- ticular, we implement different transition- based and graph-based models, as well as a parser ensembler, and evaluate their ef- fectiveness for semantic dependency pars- ing. Evaluation gauges how successful data-driven …...

The paper describes the approaches taken by the NTNU team to the SemEval 2014 Semantic Textual Similarity shared task. The solutions combine measures based on lexical soft cardinality and character n-gram feature representations with lexi- cal distance metrics from TakeLab’s base- line system. The final NTNU system is based on …...

We use a Combinatory Categorial Gram- mar (CCG) parser with a structured per- ceptron learner to address Shared Task 6 of SemEval-2014, Supervised Semantic Parsing of Robotic Spatial Commands. Our system reaches an accuracy of 79% ignoring spatial context and 87% using the spatial planner, showing that CCG can successfully …...

We describe the submission of the SAP Research & Innovation team to the Se- mEval 2014 Task 9: Sentiment Analy- sis in Twitter. We challenged ourselves to develop a competitive sentiment anal- ysis system within a very limited time frame. Our submission was developed in less than two days and …...

This paper describes state-of-the-art statis- tical systems for automatic sentiment anal- ysis of tweets. In a Semeval-2014 shared task (Task 9), our submissions obtained highest scores in the term-level sentiment classification subtask on both the 2013 and 2014 tweets test sets. In the message-level sentiment classification task, our submis- sions …...