Articles

We describe two systems that participated in SemEval-2010 task 17 (All-words Word Sense Disambiguation on a Specific Do- main) and were ranked in the third and fourth positions in the formal evaluation. Domain adaptation techniques using the background documents released in the task were used to assign ranking scores to …...

Systems using text windows to model word contexts have mostly been using fixed-sized windows and uniform weights. The window size is often selected by trial and error to maximize task results. We propose a non-supervised method for se- lecting weights for each window distance, effectively removing the need to limit …...

This paper reports about our three par- ticipating systems in SemEval-2 Japanese WSD task. The first one is a clustering based method, which chooses a sense for, not individual instances, but automatically constructed clusters of instances. The sec- ond one is a classification method, which is an ordinary SVM classifier …...

We participated in the SemEval-2010 Japanese Word Sense Disambiguation (WSD) task (Task 16) and focused on the following: (1) investigating domain differences, (2) incorporating topic fea- tures, and (3) predicting new unknown senses. We experimented with Support Vector Machines (SVM) and Maximum Entropy (MEM) classifiers. We achieved 80.1% accuracy in …...

This paper describes our infrequent sense identification system participating in the SemEval-2010 task 15 on Infrequent Sense Identification for Mandarin Text to Speech Systems. The core system is a supervised system based on the ensembles of Naïve Bayesian classifiers. In order to solve the problem of unbalanced sense distribution, we …...

We describe our language-independent un- supervised word sense induction system. This system only uses topic features to cluster different word senses in their global context topic space. Using unlabeled data, this system trains a latent Dirichlet allo- cation (LDA) topic model then uses it to infer the topics distribution of …...

The Duluth-WSI systems in SemEval-2 built word co–occurrence matrices from the task test data to create a second order co–occurrence representation of those test instances. The senses of words were in- duced by clustering these instances, where the number of clusters was automatically predicted. The Duluth-Mix system was a variation …...

A single word may have multiple un- specified meanings in a corpus. Word sense induction aims to discover these dif- ferent meanings through word use, and knowledge-lean algorithms attempt this without using external lexical resources. We propose a new method for identify- ing the different senses that uses a flexi- …...

Word sense disambiguation (WSD) is one of the main challenges in Computational Linguistics. TreeMatch is a WSD system originally developed using data from SemEval 2007 Task 7 (Coarse-grained English All- words Task) that has been adapted for use in SemEval 2010 Task 17 (All-words Word Sense Disambiguation on a Specific …...

This paper presents an unsupervised graph-based method for automatic word sense induction and disambiguation. The innovative part of our method is the as- signment of either a word or a word pair to each vertex of the constructed graph. Word senses are induced by clustering the constructed graph. In the …...

Temporal information extraction is a popular and interesting research field in the area of Natural Language Processing (NLP). In this paper, we report our works on TempEval-2 shared task. This is our first participation and we participated in all the tasks, i.e., A, B, C, D, E and F. We …...

Word sense induction and discrimination (WSID) identifies the senses of an am- biguous word and assigns instances of this word to one of these senses. We have build a WSID system that exploits syntactic and semantic features based on the results of a natural language parser component. To achieve high …...

As a participant in TempEval-2, we ad- dress the temporal relations task consist- ing of four related subtasks. We take a su- pervised machine-learning technique us- ing Markov Logic in combination with rich lexical relations beyond basic and syntactic features. One of our two submit- ted systems achieved the highest …...

We describe the University of Sheffield system used in the TempEval-2 challenge, USFD2. The challenge requires the au- tomatic identification of temporal entities and relations in text. USFD2 identifies and anchors temporal expressions, and also attempts two of the four temporal relation assignment tasks. A rule-based system picks out and …...

This paper describes the participation of Universidad Carlos III de Madrid in Task A of the TempEval-2 evaluation. The UC3M system was originally developed for the temporal expressions recognition and normalization (TERN task) in Spanish texts, according to the TIDES standard. Current version supposes an almost-total refactoring of the earliest …...

In this paper we describe a system for the recognition and normalization of temporal expressions (Task 13: TempEval-2, Task A). The recognition task is approached as a classification problem of sentence con- stituents and the normalization is imple- mented in a rule-based manner. One of the system features is extending …...

In this paper, we describe HeidelTime, a system for the extraction and normaliza- tion of temporal expressions. HeidelTime is a rule-based system mainly using regu- lar expression patterns for the extraction of temporal expressions and knowledge re- sources as well as linguistic clues for their normalization. In the TempEval-2 chal- …...

The system described in this paper has par- ticipated in the Tempeval 2 competition, specifically in the Task A, which aim is to determine the extent of the time expres- sions in a text as defined by the TimeML TIMEX3 tag, and the value of the fea- tures type and …...

This paper describes the SCHWA system entered by the University of Sydney in Se- mEval 2010 Task 12 – Parser Evaluation using Textual Entailments (Yuret et al., 2010). Our system achieved an overall ac- curacy of 70% in the task evaluation. We used the C&C parser to build CCG de- …...

We compare several state of the art dependency parsers with our own parser based on a linear classification technique. Our primary goal is therefore to use syntactic information only, in or- der to keep the comparison of the parsers as fair as possible. We demonstrate, that despite the in- ferior …...