Articles

This paper summarizes our approach to the Semeval 2007 shared task on “Classifica- tion of Semantic Relations between Nom- inals”. Our overall strategy is to develop machine-learning classifiers making use of a few easily computable and effective fea- tures, selected independently for each clas- sifier in wrapper experiments. We train …...

We are going to present a technique of preposition disambiguation based on sense discriminative patterns, which are acquired using a variant of Angluin’s al- gorithm. They represent the essential in- formation extracted from a particular type of local contexts we call Chain Clarifying Relationship contexts. The data set and the …...

In this paper we present a semantic role la- beling system submitted to the task Multi- level Semantic Annotation of Catalan and Spanish in the context of SemEval–2007. The core of the system is a memory–based classifier that makes use of full syntactic in- formation. Building on standard features, we …...

This paper describes the implementation of our three systems at SemEval-2007, for task 2 (word sense discrimination), task 5 (Chinese word sense disambiguation), and the first subtask in task 17 (English word sense disambiguation). For task 2, we ap- plied a cluster validation method to esti- mate the number of …...

We have participated in the Multilingual Chinese-English Lexical Sample Task of SemEval-2007. Our system disambiguates senses of Chinese words and finds the correct translation in English by using the web as WSD knowledge source. Since all the statistic data is obtained from search engine, the method is considered to be …...

HIT-IR-WSD is a word sense disambigua- tion (WSD) system developed for English lexical sample task (Task 11) of Semeval 2007 by Information Retrieval Lab, Harbin Institute of Technology. The system is based on a supervised method using an SVM classifier. Multi-resources including words in the surrounding context, the part- of-speech …...

Though the GYDER system has achieved the highest accuracy scores for the metonymy resolution shared task SemEval-2007 in all six subtasks, we don’tat consider the results (72.80% accuracy for org, 84.36% for loc) particularly impres- sive, and argue that metonymy resolution needs more features. 1...

We present a corpus-based supervised lear- ning system for coarse-grained sense disam- biguation. In addition to usual features for training in word sense disambiguation, our system also uses Base Level Concepts au- tomatically obtained from WordNet. Base Level Concepts are some synsets that gene- ralize a hyponymy sub–hierarchy, and pro- …...

This paper summarizes FBK-irst participa- tion at the lexical substitution task of the SEMEVAL competition. We submitted two different systems, both exploiting synonym lists extracted from dictionaries. For each word to be substituted, the systems rank the associated synonym list according to a simi- larity metric based on Latent Semantic …...

Entity disambiguation resolves the many- to-many correspondence between mentions of entities in text and unique real-world en- tities. Fair Isaac’s entity disambiguation uses language-independent entity context to agglomeratively resolve mentions with similar names to unique entities. This pa- per describes Fair Isaac’s automatic entity disambiguation capability and assesses its performance …...

This paper describes the HIT system and its participation in SemEval-2007 English Lexical Substitution Task. Two main steps are included in our method: candidate sub- stitute extraction and candidate scoring. In the first step, candidate substitutes for each target word in a given sentence are ex- tracted from WordNet. In …...

We present an approach for semantic rela- tion extraction between nominals that com- bines shallow and deep syntactic processing and semantic information using kernel meth- ods. Two information sources are consid- ered: (i) the whole sentence where the re- lation appears, and (ii) WordNet synsets and hypernymy relations of the …...

We propose an IE based approach to people disambiguation. We assume the mentioning of NEs and the relational context of a per- son in the text to be important discriminat- ing features in order to distinguish different people sharing a name. 1...

For the metonymy resolution task SemEval-2007, the use of a memory-basedat learner to train classifiers for the identifica- tion of metonymic location names is inves- tigated. Metonymy is resolved on different levels of granularity, differentiating between literal and non-literal readings on the coarse level; literal, metonymic, and mixed read- ings …...

In this paper, we present a system for Ara- bic semantic role labeling (SRL) based on SVMs and standard features. The system is evaluated on the released SEMEVAL 2007 development and test data. The results show an Fβ=1 score of 94.06 on argument bound- ary detection and an overall Fβ=1 …...

We approached the temporal relation identi- fication tasks of TempEval 2007 as pair-wise classification tasks. We introduced a va- riety of syntactically and semantically mo- tivated features, including temporal-logic- based features derived from running our Task B system on the Task A and C data. We trained support vector machine …...

This system uses a background knowledge base to identify semantic relations between base noun phrases in English text, as eva- luated in SemEval 2007, Task 4. Training data for each relation is converted to state- ments in the Scone Knowledge Representa- tion Language. At testing time a new Scone statement …...

The increasing number of web sources is exacerbating the named-entity ambiguity problem. This paper explores the use of various token-based and phrase-based fea- tures in unsupervised clustering of web pages containing personal names. From these experiments, we find that the use of rich features can significantly improve the disambiguation performance …...

For the Affective Text task at Semeval- 1/Senseval-4, the CLaC team compared a knowledge-based, domain-independent ap- proach and a standard, statistical machine learning approach to ternary sentiment an- notation of news headlines. In this paper we describe the two systems submitted to the competition and evaluate their results. We show …...

In SemEval-2007, CL Research participated in the task for Frame Semantic Structure Extraction. Participation in this task was used as the vehicle for efforts to integrate and exploit FrameNet in a comprehensive text processing system. In particular, this involved steps to build a FrameNet dictionary with CL Research’s DIMAP dictionary …...