895 citations · 2.1k across the 169 of their papers we have counts for
12 papers · 1 filter
Intrinsic Subspace Evaluation of Word Embedding Representations
Yadollah Yaghoobzadeh, Hinrich Schütze
We introduce a new methodology for intrinsic evaluation of word representations. Specifically, we identify four fundamental criteria based on the characteristics of natural languag…
Corpus-level Fine-grained Entity Typing Using Contextual Information
Yadollah Yaghoobzadeh, Hinrich Schütze
This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist". The applicatio…
Single-Model Encoder-Decoder with Explicit Morphological Representation for Reinflection
Katharina Kann, Hinrich Schütze
Morphological reinflection is the task of generating a target form given a source form, a source tag and a target tag. We propose a new way of modeling this task with neural encode…
Simple Question Answering by Attentive Convolutional Neural Network
Wenpeng Yin, Mo Yu, Bing Xiang +2
This work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Free…
Combining Recurrent and Convolutional Neural Networks for Relation Classification
Ngoc Thang Vu, Heike Adel, Pankaj Gupta +1
This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we…
Comparing Convolutional Neural Networks to Traditional Models for Slot Filling
Heike Adel, Benjamin Roth, Hinrich Schütze
We address relation classification in the context of slot filling, the task of finding and evaluating fillers like "Steve Jobs" for the slot X in "X founded Apple". We propose a co…
Discriminative Phrase Embedding for Paraphrase Identification
Wenpeng Yin, Hinrich Schütze
This work, concerning paraphrase identification task, on one hand contributes to expanding deep learning embeddings to include continuous and discontinuous linguistic phrases. On t…
Online Updating of Word Representations for Part-of-Speech Tagging
Wenpeng Yin, Tobias Schnabel, Hinrich Schütze
We propose online unsupervised domain adaptation (DA), which is performed incrementally as data comes in and is applicable when batch DA is not possible. In a part-of-speech (POS)…
Why and How to Pay Different Attention to Phrase Alignments of Different Intensities
Wenpeng Yin, Hinrich Schütze
This work studies comparatively two typical sentence pair classification tasks: textual entailment (TE) and answer selection (AS), observing that phrase alignments of different int…
Multichannel Variable-Size Convolution for Sentence Classification
Wenpeng Yin, Hinrich Schütze
We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts feat…
Attention-Based Convolutional Neural Network for Machine Comprehension
Wenpeng Yin, Sebastian Ebert, Hinrich Schütze
Understanding open-domain text is one of the primary challenges in natural language processing (NLP). Machine comprehension benchmarks evaluate the system's ability to understand t…
Ultradense Word Embeddings by Orthogonal Transformation
Sascha Rothe, Sebastian Ebert, Hinrich Schütze
Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embedding…