895 citations · 2.1k across the 169 of their papers we have counts for
23 papers · 1 filter
Learning Semantic Representations for Novel Words: Leveraging Both Form and Context
Timo Schick, Hinrich Schütze
Word embeddings are a key component of high-performing natural language processing (NLP) systems, but it remains a challenge to learn good representations for novel words on the fl…
CIS at TAC Cold Start 2015: Neural Networks and Coreference Resolution for Slot Filling
Heike Adel, Hinrich Schütze
This paper describes the CIS slot filling system for the TAC Cold Start evaluations 2015. It extends and improves the system we have built for the evaluation last year. This paper…
Multilingual Embeddings Jointly Induced from Contexts and Concepts: Simple, Strong and Scalable
Philipp Dufter, Mengjie Zhao, Hinrich Schütze
Word embeddings induced from local context are prevalent in NLP. A simple and effective context-based multilingual embedding learner is Levy et al. (2017)'s S-ID (sentence ID) meth…
Aligning Very Small Parallel Corpora Using Cross-Lingual Word Embeddings and a Monogamy Objective
Nina Poerner, Masoud Jalili Sabet, Benjamin Roth +1
Count-based word alignment methods, such as the IBM models or fast-align, struggle on very small parallel corpora. We therefore present an alternative approach based on cross-lingu…
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
Yadollah Yaghoobzadeh, Hinrich Schütze
Knowledge bases (KBs) are paramount in NLP. We employ multiview learning for increasing accuracy and coverage of entity type information in KBs. We rely on two metaviews: language…
Neural Relation Extraction Within and Across Sentence Boundaries
Pankaj Gupta, Subburam Rajaram, Hinrich Schütze +2
Past work in relation extraction mostly focuses on binary relation between entity pairs within single sentence. Recently, the NLP community has gained interest in relation extracti…
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior
Pankaj Gupta, Yatin Chaudhary, Florian Buettner +1
We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P(word|context): (1) No Language Structur…
Interpretable Textual Neuron Representations for NLP
Nina Poerner, Benjamin Roth, Hinrich Schütze
Input optimization methods, such as Google Deep Dream, create interpretable representations of neurons for computer vision DNNs. We propose and evaluate ways of transferring this t…
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting
Katharina Kann, Hinrich Schütze
Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets. We address paradigm completion, the morphological task of, given a…
Document Informed Neural Autoregressive Topic Models with Distributional Prior
Pankaj Gupta, Yatin Chaudhary, Florian Buettner +1
We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts "artificial neural…
Document Informed Neural Autoregressive Topic Models
Pankaj Gupta, Florian Buettner, Hinrich Schütze
Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks. Gen…
LISA: Explaining Recurrent Neural Network Judgments via Layer-wIse Semantic Accumulation and Example to Pattern Transformation
Pankaj Gupta, Hinrich Schütze
Recurrent neural networks (RNNs) are temporal networks and cumulative in nature that have shown promising results in various natural language processing tasks. Despite their succes…
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging
Apostolos Kemos, Heike Adel, Hinrich Schütze
Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. But these models still rely on correct token bounda…
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
Yadollah Yaghoobzadeh, Katharina Kann, Hinrich Schütze
Embedding models typically associate each word with a single real-valued vector, representing its different properties. Evaluation methods, therefore, need to analyze the accuracy…
Replicated Siamese LSTM in Ticketing System for Similarity Learning and Retrieval in Asymmetric Texts
Pankaj Gupta, Bernt Andrassy, Hinrich Schütze
The goal of our industrial ticketing system is to retrieve a relevant solution for an input query, by matching with historical tickets stored in knowledge base. A query is comprise…
News Article Teaser Tweets and How to Generate Them
Sanjeev Kumar Karn, Mark Buckley, Ulli Waltinger +1
In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers. A teaser is a short reading su…
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Wenpeng Yin, Yadollah Yaghoobzadeh, Hinrich Schütze
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question ans…
End-Task Oriented Textual Entailment via Deep Explorations of Inter-Sentence Interactions
Wenpeng Yin, Hinrich Schütze, Dan Roth
This work deals with SciTail, a natural entailment challenge derived from a multi-choice question answering problem. The premises and hypotheses in SciTail were generated with no a…
Joint Bootstrapping Machines for High Confidence Relation Extraction
Pankaj Gupta, Benjamin Roth, Hinrich Schütze
Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge…
Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages
Katharina Kann, Manuel Mager, Ivan Meza-Ruiz +1
Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neur…
Neural Architectures for Open-Type Relation Argument Extraction
Benjamin Roth, Costanza Conforti, Nina Poerner +2
In this work, we introduce the task of Open-Type Relation Argument Extraction (ORAE): Given a corpus, a query entity Q and a knowledge base relation (e.g.,"Q authored notable work…
Embedding Learning Through Multilingual Concept Induction
Philipp Dufter, Mengjie Zhao, Martin Schmitt +2
We present a new method for estimating vector space representations of words: embedding learning by concept induction. We test this method on a highly parallel corpus and learn sem…
Evaluating neural network explanation methods using hybrid documents and morphological agreement
Nina Poerner, Benjamin Roth, Hinrich Schütze
The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of…