UCB: System Description for SemEval Task #4
نویسندگان
چکیده
The UC Berkeley team participated in the SemEval 2007 Task #4, with an approach that leverages the vast size of the Web in order to build lexically-specific features. The idea is to determine which verbs, prepositions, and conjunctions are used in sentences containing a target word pair, and to compare those to features extracted for other word pairs in order to determine which are most similar. By combining these Web features with words from the sentence context, our team was able to achieve the best results for systems of category C and third best for systems of category A.
منابع مشابه
LCC-SRN: LCC's SRN System for SemEval 2007 Task 4
This document provides a description of the Language Computer Corporation (LCC) SRN System that participated in the SemEval 2007 Semantic Relation between Nominals task. The system combines the outputs of different binary and multi-class classifiers build using machine learning algorithms like Decision Trees, Semantic Scattering, Iterative Semantic Specialization, and Support Vector Machines.
متن کاملMIXCD: System Description for Evaluating Chinese Word Similarity at SemEval-2012
This document describes three systems calculating semantic similarity between two Chinese words. One is based on Machine Readable Dictionaries and the others utilize both MRDs and Corpus. These systems are performed on SemEval-2012 Task 4: Evaluating Chinese Word Similarity.
متن کاملDSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach
This paper contains the description of our participation at task 4 (sub-task A, Message Polarity Classification) of SemEval-2016. Our proposed system consists mainly of three steps. Firstly, the preprocessing step includes the tokenization and identification of special elements including URLs, hashtags, user mentions and emoticons. The second step aims at selecting and extracting the feature se...
متن کاملUKP-WSI: UKP Lab Semeval-2013 Task 11 System Description
In this paper, we describe the UKP Lab system participating in the Semeval-2013 task “Word Sense Induction and Disambiguation within an End-User Application”. Our approach uses preprocessing, co-occurrence extraction, graph clustering, and a state-of-theart word sense disambiguation system. We developed a configurable pipeline which can be used to integrate and evaluate other components for the...
متن کاملECNUCS: A Surface Information Based System Description of Sentiment Analysis in Twitter in the SemEval-2013 (Task 2)
This paper briefly reports our submissions to the two subtasks of Semantic Analysis in Twitter task in SemEval 2013 (Task 2), i.e., the Contextual Polarity Disambiguation task (an expression-level task) and the Message Polarity Classification task (a message-level task). We extract features from surface information of tweets, i.e., content features, Microblogging features, emoticons, punctuatio...
متن کامل