Articles

Discourse relation classification is an im- portant component for automatic discourse parsing and natural language understanding. The performance bottleneck of a discourse parser comes from implicit discourse rela- tions, whose discourse connectives are not overtly present. Explicit discourse connec- tives can potentially be exploited to collect more training data to …...

Dialogue systems that support users in complex problem solving must interpret user utterances within the context of a dy- namically changing, user-created problem solving artifact. This paper presents a novel approach to semantic grounding of noun phrases within tutorial dialogue for com- puter programming. Our approach per- forms joint segmentation …...

This paper presents the University of Turku entry to the SemEval-2015 task on Broad- Coverage Semantic Dependency Parsing. The system uses an existing transition-based parser as a sequence classifier to jointly predict all ar- guments of one candidate predicate at a time. Compared to our 2014 entry, the 2015 system …...

This paper presents our participation to the SemEval Task-17, related to “Taxonomy Ex- traction Evaluation” (Bordea et al., 2015). We propose a new methodology for semi- supervised and auto-supervised acquisition of lexical taxonomies from raw texts. Our ap- proach is based on the theory of pretopology which offers a powerful …...

For the purposes of SemEval-2015 Task-18 on the semantic dependency parsing we com- bined the best-performing closed track ap- proach from the SemEval-2014 competition with state-of-the-art techniques for FrameNet semantic parsing. In the closed track our sys- tem ranked third for the semantic graph accu- racy and first for exact …...

This paper describes the system submitted by the TALN-UPF team to SEMEVAL Task 17 (Taxonomy Extraction Evaluation). We present a method for automatically learning a taxonomy from a flat terminology, which benefits from a definition corpus obtained by querying the BabelNet semantic network. Then, we combine a machine-learning al- gorithm …...

This paper describes our contribution to the SemEval-2015 task 17 on “Taxonomy Extrac- tion Evaluation”. We propose a hypernym de- tection system combining three modules: a lexico-syntactic pattern matcher, a morpho- syntactic analyzer and a module retrieving hy- pernym relations from structured lexical re- sources. Our system ranked first in …...

Taxonomy structures are important tools in the science of classification of things or concepts, including the principles that un- derlie such classification. This paper pre- sents an approach to the problem of taxonomy construction from texts focusing on the hyponym-hypernym relation between two terms. Given a set of terms in …...

This paper describes the USAAR-WLV tax- onomy induction system that participated in the Taxonomy Extraction Evaluation task of SemEval-2015. We extend prior work on using vector space word embedding models for hypernym-hyponym extraction by simpli- fying the means to extract a projection matrix that transforms any hyponym to its hypernym. …...

This paper is a description of our system for SemEval-2015 Task 18: Broad-Coverage Semantic Dependency Parsing. We imple- ment a hybrid parser which benefits from both transition-based and graph-based parsing ap- proaches. In particular, the tree approximation method is explored to take advantage of well- studied tree parsing techniques. Evaluation …...

For information retrieval, it is useful to classi- fy documents using a hierarchy of terms from a domain. One problem is that, for many do- mains, hierarchies of terms are not available. The task 17 of SemEval 2015 addresses the problem of structuring a set of terms from a given …...

This paper describes the first shared task on Taxonomy Extraction Evaluation organ- ised as part of SemEval-2015. Participants were asked to find hypernym-hyponym re- lations between given terms. For each of the four selected target domains the partici- pants were provided with two lists of domain- specific terms: a WordNet …...

In this paper we describe the SpRL-CWW en- try into SemEval 2015: Task 8 SpaceEval. It detects spatial and motion relations as de- fined by the ISO-Space specifications in two phases: (1) it detects spatial elements and spa- tial/motion signals with a Conditional Ran- dom Field model that uses a …...

Human languages exhibit a variety of strate- gies for communicating spatial information, including toponyms, spatial nominals, loca- tions that are described in relation to other lo- cations, and movements along paths. SpaceE- val is a combined information extraction and classification task with the goal of identify- ing and categorizing such …...

This paper describes the USAAR-CHRONOS participation in the Diachronic Text Evalua- tion task of SemEval-2015 to identify the time period of historical text snippets. We adapt a web crawler to retrieve the original source of the text snippets and determine the publi- cation year of the retrieved texts from their …...

This paper presents X-Space, a system that fol- lows the ISO-Space annotation scheme in or- der to capture spatial information as well as our contribution to the SemEval-2015 task 8 (SpaceEval). Our system is the only partici- pant system that reported results for all three evaluation configurations in SpaceEval. 1...

SpaceEval (SemEval 2015 Task 8), which concerns spatial information extraction, builds on the spatial role identification tasks intro- duced in SemEval 2012 and used in SemEval 2013. Among the host of subtasks presented in SpaceEval, we participated in subtask 3a, which focuses solely on spatial relation ex- traction. To address …...

In this paper we describe a novel task, namely the Diachronic Text Evaluation task. A cor- pus of snippets which contain relevant infor- mation for the time when the text was created is extracted from a large collection of newspa- pers published between 1700 and 2010. The task, subdivided in …...

We present our submission to SemEval-2015 Task 7: Diachronic Text Evaluation, in which we approach the task of assigning a date to a text as a multi-class classification problem. We extract n-gram features from the text at the letter, word, and syntactic level, and use these to train a classifier …...

This paper describes the AMBRA system, entered in the SemEval-2015 Task 7: ‘Di- achronic Text Evaluation’ subtasks one and two, which consist of predicting the date when a text was originally written. The task is valuable for applications in digital humani- ties, information systems, and historical lin- guistics. The novelty …...