MayoClinicNLP-CORE: Semantic representations for textual similarity
نویسندگان
چکیده
The Semantic Textual Similarity (STS) task examines semantic similarity at a sentencelevel. We explored three representations of semantics (implicit or explicit): named entities, semantic vectors, and structured vectorial semantics. From a DKPro baseline, we also performed feature selection and used sourcespecific linear regression models to combine our features. Our systems placed 5th, 6th, and 8th among 90 submitted systems.
منابع مشابه
Determining Semantic Textual Similarity using Natural Deduction Proofs
Determining semantic textual similarity is a core research subject in natural language processing. Since vector-based models for sentence representation often use shallow information, capturing accurate semantics is difficult. By contrast, logical semantic representations capture deeper levels of sentence semantics, but their symbolic nature does not offer graded notions of textual similarity. ...
متن کاملiKernels-Core: Tree Kernel Learning for Textual Similarity
This paper describes the participation of iKernels system in the Semantic Textual Similarity (STS) shared task at *SEM 2013. Different from the majority of approaches, where a large number of pairwise similarity features are used to learn a regression model, our model directly encodes the input texts into syntactic/semantic structures. Our systems rely on tree kernels to automatically extract a...
متن کاملKLUE-CORE: A regression model of semantic textual similarity
This paper describes our system entered for the *SEM 2013 shared task on Semantic Textual Similarity (STS). We focus on the core task of predicting the semantic textual similarity of sentence pairs. The current system utilizes machine learning techniques trained on semantic similarity ratings from the *SEM 2012 shared task; it achieved rank 20 out of 90 submissions from 35 different teams. Give...
متن کاملASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity
A growing body of research has recently been conducted on semantic textual similarity using a variety of neural network models. While recent research focuses on word-based representation for phrases, sentences and even paragraphs, this study considers an alternative approach based on character n-grams. We generate embeddings for character n-grams using a continuous-bag-of-n-grams neural network...
متن کاملProbabilistic Soft Logic for Semantic Textual Similarity
Probabilistic Soft Logic (PSL) is a recently developed framework for probabilistic logic. We use PSL to combine logical and distributional representations of natural-language meaning, where distributional information is represented in the form of weighted inference rules. We apply this framework to the task of Semantic Textual Similarity (STS) (i.e. judging the semantic similarity of naturallan...
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 2013