Articles

This paper describes the system submit- ted for the Sentiment Analysis in Twitter Task of SEMEVAL 2014 and specifically the Message Polarity Classification sub- task. We used a 2–stage pipeline approach employing a linear SVM classifier at each stage and several features including mor- phological features, POS tags based fea- …...

The Tag&Parse approach to semantic parsing first assigns semantic tags to each word in a sentence and then parses the tag sequence into a semantic tree. We use statistical approach for tagging, pars- ing, and reference resolution stages. Each stage produces multiple hypotheses which are re-ranked using spatial validation. We …...

This paper describes the systems deployed by the ALPAGE team to participate to the SemEval-2014 Task on Broad-Coverage Semantic Dependency Parsing. We de- veloped two transition-based dependency parsers with extended sets of actions to handle non-planar acyclic graphs. For the open track, we worked over two orthog- onal axes – …...

In this paper, we describe our unsupervised method submitted to the Cross-Level Se- mantic Similarity task in Semeval 2014 that computes semantic similarity between two different sized text fragments. Our method models each text fragment by using the co- occurrence statistics of either occurred words or their substitutes. The co-occurrence …...

In Semantic Textual Similarity, systems rate the degree of semantic equivalence between two text snippets. This year, the participants were challenged with new data sets for English, as well as the in- troduction of Spanish, as a new language in which to assess semantic similarity. For the English subtask, we …...

We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year’s task that ran successfully as part of SemEval- 2013. As in 2013, this was the most popu- lar SemEval task; a total of 46 teams con- tributed 27 submissions …...

In this paper we describe the ASAP sys- tem (Automatic Semantic Alignment for Phrases)1 which participated on the Task 1 at the SemEval-2014 contest (Marelli et al., 2014a). Our assumption is that STS (Semantic Text Similarity) follows a func- tion considering lexical, syntactic, seman- tic and distributional features. We demon- …...

This paper describes the SemEval-2014, Task 7 on the Analysis of Clinical Text and presents the evaluation results. It fo- cused on two subtasks: (i) identification (Task A) and (ii) normalization (Task B) of diseases and disorders in clinical reports as annotated in the Shared Annotated Re- sources (ShARe)1 corpus. …...

SemEval-2014 Task 6 aims to advance semantic parsing research by providing a high-quality annotated dataset to com- pare and evaluate approaches. The task focuses on contextual parsing of robotic commands, in which the additional con- text of spatial scenes can be used to guide a parser to control a robot …...

We present a new cross-lingual task for SemEval concerning the translation of L1 fragments in an L2 context. The task is at the boundary of Cross-Lingual Word Sense Disambiguation and Machine Translation. It finds its application in the field of computer-assisted translation, par- ticularly in the context of second language …...

Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint. The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, irrespective of the entities mentioned (e.g., laptops) and their aspects (e.g., battery, screen). SemEval- …...

This paper introduces a new SemEval task on Cross-Level Semantic Similarity (CLSS), which measures the degree to which the meaning of a larger linguistic item, such as a paragraph, is captured by a smaller item, such as a sentence. High- quality data sets were constructed for four comparison types using …...

In this paper we present the SemEval- 2014 Task 2 on spoken dialogue gram- mar induction. The task is to classify a lexical fragment to the appropriate se- mantic category (grammar rule) in order to construct a grammar for spoken dia- logue systems. We describe four sub- tasks covering two …...

This paper presents the task on the evalu- ation of Compositional Distributional Se- mantics Models on full sentences orga- nized for the first time within SemEval- 2014. Participation was open to systems based on any approach. Systems were pre- sented with pairs of sentences and were evaluated on their ability …...

This paper presents V3, an unsupervised system for aspect-based Sentiment Analy- sis when evaluated on the SemEval 2014 Task 4. V3 focuses on generating a list of aspect terms for a new domain using a collection of raw texts from the domain. We also implement a very basic approach to …...

This paper describes Team UWM’s sys- tem for the Task 7 of SemEval 2014 that does disorder mention extraction and nor- malization from clinical text. For the dis- order mention extraction (Task A), the sys- tem was trained using Conditional Ran- dom Fields with features based on words, their POS …...

This paper describes Team UWM’s sys- tem for the Task 6 of SemEval 2014 for doing supervised semantic parsing of robotic spatial commands. An existing semantic parser, KRISP, was trained us- ing the provided training data of natural language robotic spatial commands paired with their meaning representations in the formal …...

This paper describes our system partici- pating in the aspect-based sentiment anal- ysis task of Semeval 2014. The goal was to identify the aspects of given tar- get entities and the sentiment expressed to- wards each aspect. We firstly introduce a system based on supervised machine learning, which is strictly …...

This paper describes a deep-parsing ap- proach to SemEval-2014 Task 6, a novel context-informed supervised parsing and semantic analysis problem in a controlled domain. The system comprises a hand- built rule-based solution based on a pre- existing broad coverage deep grammar of English, backed up by a off-the-shelf data- driven …...

In this paper we present our system par- ticipating in the SemEval-2014 Task 7 in both subtasks A and B, aiming at recognizing and normalizing disease and symptom mentions from electronic medi- cal records respectively. In subtask A, we used an existing NER system, NERsuite, with our own feature set …...