Articles

This paper presents our contribution to the SemEval-2015 Task 7. The task was subdi- vided into three subtasks that consisted of au- tomatically identifying the time period when a piece of news was written (1,2) as well as automatically determining whether a specific phrase in a sentence is relevant or …...

We present two approaches to time expres- sion identification, as entered in to SemEval- 2015 Task 6, Clinical TempEval. The first is a comprehensive rule-based approach that favoured recall, and which achieved the best recall for time expression identification in Clin- ical TempEval. The second is an SVM-based system built …...

This paper presents the HITSZ-ICRC system designed for the QA TempEval challenge in SemEval-2015. The system used an integra- tion approach to annotate temporal informa- tion by merging temporal annotation results from different temporal annotators. TIPSemB, ClearTK and TARSQI were used as temporal annotators to get candidate temporal annota- tion …...

Building unified timelines from a collection of written news articles requires cross-document event coreference resolution and temporal re- lation extraction. In this paper we present an approach event coreference resolution accord- ing to: a) similar temporal information, and b) similar semantic arguments. Temporal in- formation is detected using an automatic …...

Clinical TempEval 2015 brought the tempo- ral information extraction tasks of past Temp- Eval campaigns to the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identifica- tion. Participant systems were trained and eval- uated on a corpus of clinical notes …...

The 2015 Clinical TempEval Challenge ad- dressed the problem of temporal reasoning in the clinical domain by providing an anno- tated corpus of pathology and clinical notes related to colon cancer patients. The chal- lenge consisted of six subtasks: TIMEX3 and event span detection, TIMEX3 and event at- tribute classification, …...

The HLT-FBK system is a suite of SVMs- based classification models for extracting time expressions, events and temporal relations, each with a set of features obtained with the NewsReader NLP pipeline. HLT-FBK’s best system runs ranked 1st in all three domains, with a recall of 0.30 over all domains. Our …...

In this paper, we give an overview of our participation in the timeline generation task of SemEval-2015 (task 4, TimeLine: Cross- Document Event Ordering). The main goals of this new track are, given a collection of news articles and a so-called target entity, to determine events that are relevant for …...

QA TempEval shifts the goal of previous TempEvals away from an intrinsic evaluation methodology toward a more extrinsic goal of question answering. This evaluation requires systems to capture temporal information rele- vant to perform an end-user task, as opposed to corpus-based evaluation where all temporal information is equally important. Evaluation …...

This paper describes the system SPINOZA VU developed for the SemEval 2015 Task 4: Cross Document TimeLines. The system integrates output from the News- Reader Natural Language Processing pipeline and is designed following an entity based model. The poor performance of the submit- ted runs are mainly a consequence of …...

This paper describes the outcomes of the TimeLine task (Cross-Document Event Order- ing), that was organised within the Time and Space track of SemEval-2015. Given a set of documents and a set of target entities, the task consisted of building a timeline for each en- tity, by detecting, anchoring in …...

This paper describes our participation in SemEval-2015 Task 12, and the opinion min- ing system sentiue. The general idea is that systems must determine the polarity of the sentiment expressed about a certain aspect of a target entity. For slot 1, entity and attribute category detection, our system applies a …...

Following the footsteps of SemEval-2014 Task 4 (Pontiki et al., 2014), SemEval-2015 too had a task dedicated to aspect-level senti- ment analysis (Pontiki et al., 2015), which saw participation from over 25 teams. In Aspect- based Sentiment Analysis, the aim is to iden- tify the aspects of entities and the …...

This paper describes the system we submitted to In-domain ABSA subtask of SemEval 2015 shared task on aspect-based sentiment analy- sis that includes aspect category detection and sentiment polarity classification. For the as- pect category detection, we combined an SVM classifier with implicit aspect indicators. For the sentiment polarity classification, …...

This paper provides a detailed description of the approach of our system for the Aspect- Based Sentiment Analysis task of SemEval- 2015. The task is to identify the Aspect Cate- gory (Entity and Attribute pair), Opinion Tar- get and Sentiment of the reviews. For the In-domain subtask that is provided …...

This paper presents a supervised Aspect Based Sentiment Analysis (ABSA) system. Our aim is to develop a modular platform which allows to easily conduct experiments by replacing the modules or adding new features. We obtain the best result in the Opinion Target Extrac- tion (OTE) task (slot 2) using an …...

This paper describes our contribution in Opin- ion Target Extraction OTE and Sentiment Po- larity sub tasks of SemEval 2015 ABSA task. A CRF model with IOB notation has been adopted for OTE with several groups of fea- tures including syntactic, lexical, semantic, sentiment lexicon features. Our submission for OTE …...

This paper describes our systems submitted to the target-dependent sentiment polarity clas- sification subtask in aspect based sentimen- t analysis (ABSA) task (i.e., Task 12) in Se- mEval 2015. To settle this problem, we ex- tracted several effective features from three se- quential sentences, including sentiment lexi- con, linguistic and …...

This paper describes the participation of the SINAI research group in the task Aspect Based Sentiment Analysis of SemEval Work- shop 2015 Edition. We propose a syntactic approach for identifying the words that mod- ify each aspect, with the aim of classifying the sentiment expressed towards each attribute of an …...

This paper reports on our participation in SemEval-2015 Task 12, which was devoted to Aspect-Based Sentiment Analysis. Partic- ipants were required to identify the category (entity and attribute), the opinion target, and the polarity of customer reviews. The system we built relies on classification algorithms to identify aspect categories and …...