Articles

Semantic Textual Similarity (STS) measures the degree of semantic equivalence between two texts. This paper presents the results of the STS pilot task in Semeval. The training data contained 2000 sentence pairs from pre- viously existing paraphrase datasets and ma- chine translation evaluation resources. The test data also comprised 2000 …...

The paper presents the SemEval-2012 Shared Task 5: Chinese Semantic Dependency Pars- ing. The goal of this task is to identify the de- pendency structure of Chinese sentences from the semantic view. We firstly introduce the motivation of providing Chinese semantic de- pendency parsing task, and then describe the task …...

This task focuses on evaluating word similari- ty computation in Chinese. We follow the way of Finkelstein et al. (2002) to select word pairs. Then we organize twenty under- graduates who are major in Chinese linguis- tics to annotate the data. Each pair is assigned a similarity score by each …...

This SemEval2012 shared task is based on a recently introduced spatial annotation scheme called Spatial Role Labeling. The Spatial Role Labeling task concerns the extraction of main components of the spatial semantics from nat- ural language: trajectors, landmarks and spa- tial indicators. In addition to these major components, the links …...

Up to now, work on semantic relations has fo- cused on relation classification: recognizing whether a given instance (a word pair such as virus:flu) belongs to a specific relation class (such as CAUSE:EFFECT). However, instances of a single relation class may still have signif- icant variability in how characteristic they …...

We describe the English Lexical Simplifica- tion task at SemEval-2012. This is the first time such a shared task has been organized and its goal is to provide a framework for the evaluation of systems for lexical simplification and foster research on context-aware lexical simplification approaches. The task requires that …...

Automatic detection of negation cues along with their scope and corresponding negated events is an important task that could bene- fit other natural language processing (NLP) tasks such as extraction of factual information from text, sentiment analysis, etc. This paper presents a system for this task that exploits phrasal and …...

This paper reports on a simple system for resolving the scope of negation in the closed track of the *SEM 2012 Shared Task. Cue detection is performed using regular expression rules extracted from the training data. Both scope tokens and negated event tokens are resolved using a Conditional Random Field …...

In this paper, we present a system for de- tecting negation in English text. We address three tasks: negation cue detection, negation scope resolution and negated event identifi- cation. We pose these tasks as sequence la- beling problems. For each task, we train a Conditional Random Field (CRF) model on …...

This paper describes the first of two systems submitted from the University of Oslo (UiO) to the 2012 *SEM Shared Task on resolving negation. Our submission is an adaption of the negation system of Velldal et al. (2012), which combines SVM cue classification with SVM-based ranking of syntactic constituents for …...

Our system breaks down the problem of rank- ing a list of lexical substitutions according to how simple they are in a given context into a series of pairwise comparisons between can- didates. For this we learn a binary classifier. As only very little training data is provided, we describe …...

This paper describes the second of two sys- tems submitted from the University of Oslo (UiO) to the 2012 *SEM Shared Task on re- solving negation. The system combines SVM cue classification with CRF sequence labeling of events and scopes. Models for scopes and events are created using lexical and …...

We use the NLP toolchain that is used to con- struct the Groningen Meaning Bank to address the task of detecting negation cue and scope, as defined in the shared task “Resolving the Scope and Focus of Negation”. This toolchain applies the C&C tools for parsing, using the formalism of …...

Simply detecting negation cues is not suffi- cient to determine the semantics of negation, scope and focus must be taken into account. While scope detection has recently seen re- peated attention, the linguistic notion of focus is only now being introduced into computa- tional work. The *Sem2012 Shared Task is …...

UCM-2 infers the words that are affected by negations by browsing dependency syntactic structures. It first makes use of an algo- rithm that detects negation cues, like no, not or nothing, and the words affected by them by traversing Minipar dependency structures. Second, the scope of these negation cues is …...

This paper presents one of the two contribu- tions from the Universidad Complutense de Madrid to the *SEM Shared Task 2012 on Re- solving the Scope and Focus of Negation. We describe a rule-based system for detecting the presence of negations and delimitating their scope. It was initially intended for …...

This paper describes our participation in the closed track of the *SEM 2012 Shared Task of finding the scope of negation. To perform the task, we propose a system that has three components: negation cue detection, scope of negation detection, and negated event detec- tion. In the first phase, the …...

The Joint Conference on Lexical and Compu- tational Semantics (*SEM) each year hosts a shared task on semantic related topics. In its first edition held in 2012, the shared task was dedicated to resolving the scope and focus of negation. This paper presents the specifica- tions, datasets and evaluation criteria …...

We investigate the effects of adding semantic annotations including word sense hypernyms to the source text for use as an extra source of information in HPSG parse ranking for the English Resource Grammar. The semantic an- notations are coarse semantic categories or en- tries from a distributional thesaurus, assigned either …...

Detecting emotions in microblogs and so- cial media posts has applications for industry, health, and security. However, there exists no microblog corpus with instances labeled for emotions for developing supervised systems. In this paper, we describe how we created such a corpus from Twitter posts using emotion- word hashtags. We …...