BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual Similarity
نویسندگان
چکیده
In this paper we present the evaluation of different features for multiligual and crosslevel semantic textual similarity. Three different types of features were used: lexical, knowledge-based and corpus-based. The results obtained at the Semeval competition rank our approaches above the average of the rest of the teams highlighting the usefulness of the features presented in this paper.
منابع مشابه
BUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment
The results obtained by the BUAP team at Task 1 of SemEval 2014 are presented in this paper. The run submitted is a supervised version based on two classification models: 1) We used logistic regression for determining the semantic relatedness between a pair of sentences, and 2) We employed support vector machines for identifying textual entailment degree between the two sentences. The behaviour...
متن کاملMeerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity Systems
We describe UMBC’s systems developed for the SemEval 2014 tasks on Multilingual Semantic Textual Similarity (Task 10) and Cross-Level Semantic Similarity (Task 3). Our best submission in the Multilingual task ranked second in both English and Spanish subtasks using an unsupervised approach. Our best systems for Cross-Level task ranked second in Paragraph-Sentence and first in both Sentence-Phra...
متن کاملBUAP: Lexical and Semantic Similarity for Cross-lingual Textual Entailment
In this paper we present a report of the two different runs submitted to the task 8 of Semeval 2012 for the evaluation of Cross-lingual Textual Entailment in the framework of Content Synchronization. Both approaches are based on textual similarity, and the entailment judgment (bidirectional, forward, backward or no entailment) is given based on a set of decision rules. The first approach uses t...
متن کاملECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
To model semantic similarity for multilingual and cross-lingual sentence pairs, we first translate foreign languages into English, and then build an efficient monolingual English system with multiple NLP features. Our system is further supported by deep learning models and our best run achieves the mean Pearson correlation 73.16% in primary track.
متن کاملExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity
We present ExB Themis – a word alignmentbased semantic textual similarity system developed for SemEval-2015 Task 2: Semantic Textual Similarity. It combines both string and semantic similarity measures as well as alignment features using Support Vector Regression. It occupies the first three places on Spanish data and additionally places second on English data. ExB Themis proved to be the best ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2014