Articles

We describe our submission system to the SemEval-2016 Task 8 on Abstract Meaning Representation (AMR) Parsing. We attempt to improve AMR parsing by exploiting prepo- sition semantic role labeling information re- trieved from a multi-layer feed-forward neu- ral network. Prepositional semantics is in- cluded as features into the transition-based AMR …...

This paper describes our contribution to the SemEval 2016 Workshop. We participated in the Shared Task 8 on Meaning Representa- tion parsing using a transition-based approach, which builds upon the system of Wang et al. (2015a) and Wang et al. (2015b), with addi- tions that utilize a Feedforward Neural Net- …...

This is a system description paper for a sub- mission to Task 8 of SemEval-2016: Meaning Representation Parsing. No use was made of the training data provided by the task. Instead existing components were combined to form a pipeline able to take raw sentences as input and output meaning representations. …...

Two extensions to the AMR smatch scoring script are presented. The first extension com- bines the smatch scoring script with the C6.0 rule-based classifier to produce a human-read- able report on the error patterns frequency ob- served in the scored AMR graphs. This first ex- tension results in 4% gain …...

SemEval 2016 Task 12 addresses temporal reasoning in the clinical domain. In this paper, we present our participation for relation ex- traction based on gold standard entities (sub- tasks DR and CR). We used a supervised approach comparing plain lexical features to word embeddings for temporal relation identi- fication, and …...

This paper reports the IIT-TUDA participation in the SemEval 2016 shared Task 5 of Aspect Based Sentiment Analysis (ABSA) for sub- task 1. We describe our system incorporat- ing domain dependency graph features, dis- tributional thesaurus and unsupervised lexical induction using an unlabeled external corpus for aspect based sentiment analysis. …...

This paper describes CAMR, the transition- based parser that we use in the SemEval-2016 Meaning Representation Parsing task. The main contribution of this paper is a descrip- tion of the additional sources of information that we use as features in the parsing model to further boost its performance. We start …...

In this paper, we propose a classifier for predicting message-level sentiments of En- glish micro-blog messages from Twitter. Our method builds upon the convolutional sen- tence embedding approach proposed by (Sev- eryn and Moschitti, 2015a; Severyn and Mos- chitti, 2015b). We leverage large amounts of data with distant supervision to …...

In this paper we present our system de- veloped for the SemEval 2016 Task 2 - Interpretable Semantic Textual Similarity along with the results obtained for our sub- mitted runs. Our system participated in the subtasks predicting chunk similarity alignments for gold chunks as well as for predicted chunks. The …...

This paper describes the KeLP system par- ticipating in the SemEval-2016 Community Question Answering (cQA) task. The chal- lenge tasks are modeled as binary classifi- cation problems: kernel-based classifiers are trained on the SemEval datasets and their scores are used to sort the instances and pro- duce the final ranking. …...

We describe an attention-based convolutional neural network for the English semantic tex- tual similarity (STS) task in the SemEval- 2016 competition (Agirre et al., 2016). We develop an attention-based input interaction layer and incorporate it into our multi- perspective convolutional neural network (He et al., 2015), using the PARAGRAM-PHRASE word …...

Manually constructed taxonomies provide a crucial resource for many NLP technologies, yet these resources are often limited in their lexical coverage due to their construction pro- cedure. While multiple approaches have been proposed to enrich such taxonomies with new concepts, these techniques are typically eval- uated by measuring the accuracy …...

This paper describes the second edition of the shared task on Taxonomy Extraction Evalua- tion organised as part of SemEval 2016. This task aims to extract hypernym-hyponym rela- tions between a given list of domain-specific terms and then to construct a domain taxon- omy based on them. TExEval-2 introduced a …...

This paper describes the SemEval-2016 Shared Task 9: Chinese semantic Depen- dency Parsing. We extend the traditional tree- structured representation of Chinese sentence to directed acyclic graphs that can capture richer latent semantics, and the goal of this task is to identify such semantic structures from a corpus of Chinese …...

Clinical TempEval 2016 evaluated temporal information extraction systems on the clinical domain. Nine sub-tasks were included, cover- ing problems in time expression identification, event expression identification and temporal re- lation identification. Participant systems were trained and evaluated on a corpus of clinical and pathology notes from the Mayo Clinic, an- …...

We introduce a word frequency-based classi- fier for the SemEval 2016 complex word iden- tification task (#11). Words with lower fre- quency are predicted as complex based on a threshold optimized for G-score. We exam- ine three different corpora for calculating fre- quencies and find English Wikipedia to per- form …...

We investigate the usage of word embeddings, namely Glove and SCODE, along with sub- string features on Complex Word Identifica- tion task. We introduce two systems: the first system utilizes the word embeddings of the target word and its substrings as features while the other considers the context infor- mation …...

In this paper, we present our system devel- oped for the SemEval 2016 Task 11: Com- plex Word Identification. Our team achieved the 3rd place among 21 participants. Our sys- tems ranked 4th and 13th among 42 submitted systems. We proposed multiple features suit- able for complex word identification, evalu- …...

We present two systems created for SemEval- 2016s Task 11: Complex Word Identification. Our two systems, a regression tree and de- cision tree, were trained with a word’s uni- gram and lemma word counts, average age- of-acquisition, and a measure of concreteness. The systems ranked 5th and 6th, respectively, on …...

We present two methods for the automatic de- tection of complex words in context as per- ceived by non-native English readers, for the SemEval 2016 Task 11 on Complex Word Identification (Paetzold and Specia, 2016). The submitted systems exploit the same set of features, but are highly disparate in (i) …...