Ағаш ядролары

Convolution kernels, such as sequence and tree ker- nels, are advantageous for both the concept and ac- curacy of many natural language processing (NLP) tasks. Experiments have, however, shown that …...

We extend previous work on tree kernels to estimate the similarity between the dependency trees of sen- tences. Using this kernel within a Support Vector Machine, we detect and classify …...

We present a method for speeding up the calculation of tree kernels during train- ing. The calculation of tree kernels is still heavy even with efficient dynamic pro- gramming (DP) …...

In recent years tree kernels have been pro- posed for the automatic learning of natural language applications. Unfortunately, they show (a) an inherent super linear complex- ity and (b) a …...

The combination of Support Vector Machines with very high dimensional kernels, such as string or tree kernels, suffers from two ma- jor drawbacks: first, the implicit representa- tion of feature …...

In this paper, we describe an approach based on off-the-shelf parsers and semantic re- sources for the Recognizing Textual Entail- ment (RTE) challenge that can be generally applied to any …...

This paper describes Uppsala University’s submissions to the Quality Estimation (QE) shared task at WMT 2012. We present a QE system based on Support Vector Machine re- gression, using a …...

We present work on linking events and flu- ents (i.e., relations that hold for certain periods of time) to temporal information in text, which is an important enabler for many …...

This paper proposes a framework for automat- ically engineering features for two important tasks of question answering: answer sentence selection and answer extraction. We represent question and answer sentence pairs …...

In this paper, we present a discrimina- tive approach for reranking discourse trees generated by an existing probabilistic dis- course parser. The reranker relies on tree kernels (TKs) to capture …...

AbstractStructural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches …...

A key challenge introduced by the re- cent SpaceEval shared task on spatial re- lation extraction is the identification of MOVELINKs, a type of spatial relation in which up to …...

Previous work on Automatic Paraphrase Iden- tification (PI) is mainly based on modeling text similarity between two sentences. In con- trast, we study methods for automatically de- tecting whether a …...

AbstractIn real-world data, e.g., from Web forums, text is often contaminated with redundant or irrelevant content, which leads to introducing noise in machine learning algorithms. In this paper, we apply …...

AbstractTree kernels (TKs) and neural networks are two effective approaches for automatic feature engineering. In this paper, we combine them by modeling context word similarity in semantic TKs. This way, …...

AbstractRecent work has shown that Tree Kernels (TKs) and Convolutional Neural Networks (CNNs) obtain the state of the art in answer sentence reranking. Additionally, their combination used in Support Vector …...