Articles

This work introduces a new unsupervised approach to multilingual word sense disambiguation. Its main purpose is to automatically choose the intended sense (meaning) of a word in a particular context for different languages. It does so by selecting the correct Babel synset for the word and the various Wiki Page …...

This paper presents the SemEval-2013 task on multilingual Word Sense Disambiguation. We describe our experience in producing a mul- tilingual sense-annotated corpus for the task. The corpus is tagged with BabelNet 1.1.1, a freely-available multilingual encyclopedic dictionary and, as a byproduct, WordNet 3.0 and the Wikipedia sense inventory. We present …...

In this paper we describe our system used to participate in the Student-Response-Analysis task-7 at SemEval 2013. This system is based on text overlap through the soft cardinality and a new mechanism for weight propagation. Al- though there are several official performance measures, taking into account the overall ac- curacy …...

This paper describes our system for Task 11 of SemEval-2013. In the task, participants are provided with a set of ambiguous search queries and the snippets returned by a search engine, and are asked to associate senses with the snippets. The snippets are then clus- tered using the sense assignments …...

In this paper, we describe the UKP Lab sys- tem participating in the Semeval-2013 task “Word Sense Induction and Disambiguation within an End-User Application”. Our ap- proach uses preprocessing, co-occurrence ex- traction, graph clustering, and a state-of-the- art word sense disambiguation system. We developed a configurable pipeline which can be …...

Many NLP applications require information about locations of objects referenced in text, or relations between them in space. For ex- ample, the phrase a book on the desk contains information about the location of the object book, as trajector, with respect to another ob- ject desk, as landmark. Spatial Role …...

The aim of this paper is to perform Word Sense induction (WSI); which clusters web search results and produces a diversified list of search results. It describes the WSI system de- veloped for Task 11 of SemEval - 2013. This paper implements the idea of monotone sub- modular function optimization …...

The Duluth systems that participated in task 11 of SemEval–2013 carried out word sense induction (WSI) in order to cluster Web search results. They relied on an approach that repre- sented Web snippets using second–order co– occurrences. These systems were all imple- mented using SenseClusters, a freely available open source …...

In this paper we describe our Semeval-2013 task on Word Sense Induction and Dis- ambiguation within an end-user application, namely Web search result clustering and diver- sification. Given a target query, induction and disambiguation systems are requested to clus- ter and diversify the search results returned by a search engine …...

This paper describes the NRC submission to the Spanish Cross-Lingual Word Sense Dis- ambiguation task at SemEval-2013. Since this word sense disambiguation task uses Spanish translations of English words as gold annota- tion, it can be cast as a machine translation problem. We therefore submitted the output of a standard …...

We describe the LIMSI system for the SemEval-2013 Cross-lingual Word Sense Dis- ambiguation (CLWSD) task. Word senses are represented by means of translation clusters in different languages built by a cross-lingual Word Sense Induction (WSI) method. Our CLWSD classifier exploits the WSI output for selecting appropriate translations for target words …...

We present our entries for the SemEval- 2013 cross-language word-sense disambigua- tion task (Lefever and Hoste, 2013). We submitted three systems based on classifiers trained on local context features, with some elaborations. Our three systems, in increasing order of complexity, were: maximum entropy classifiers trained to predict the desired target- …...

This paper describes the XLING system partici- pation in SemEval-2013 Crosslingual Word Sense Disambiguation task. The XLING system introduces a novel approach to skip the sense disambiguation step by matching query sentenc- es to sentences in a parallel corpus using topic models; it returns the word alignments as the translation …...

The goal of the Cross-lingual Word Sense Disam- biguation task is to evaluate the viability of multilin- gual WSD on a benchmark lexical sample data set. The traditional WSD task is transformed into a mul- tilingual WSD task, where participants are asked to provide contextually correct translations of English ambiguous …...

This paper presents a system for automatically generating a set of plausible paraphrases for a given noun compound and rank them in de- creasing order of their usage represented by the confidence value provided by the human annotators. Our system implements a corpus- driven probabilistic co-occurrence based model for predicting …...

This paper describes the system submitted by the MELODI team for the SemEval-2013 Task 4: Free Paraphrases of Noun Compounds (Hendrickx et al., 2013). Our approach com- bines the strength of an unsupervised distri- butional word space model with a supervised maximum-entropy classification model; the distributional model yields a feature …...

This paper presents an approach for gener- ating free paraphrases of compounds (task 4 at SemEval 2013) by decomposing the train- ing data into a collection of templates and fillers and recombining/scoring these based on a generative language model and discrimina- tive MaxEnt reranking. The system described in this paper …...

We present our system WSD2 which partic- ipated in the Cross-Lingual Word-Sense Dis- ambiguation task for SemEval 2013 (Lefever and Hoste, 2013). The system closely resem- bles our winning system for the same task in SemEval 2010. It is based on k-nearest neigh- bour classifiers which map words with local …...

In this paper, we describe SemEval-2013 Task 4: the definition, the data, the evaluation and the results. The task is to capture some of the meaning of English noun compounds via para- phrasing. Given a two-word noun compound, the participating system is asked to produce an explicitly ranked list of …...

This paper describes The University of Mel- bourne NLP group submission to the Cross- lingual Textual Entailment shared task, our first tentative attempt at the task. The ap- proach involves using parallel corpora and au- tomatic word alignment to align text fragment pairs, and statistics based on unaligned words as …...