Articles

Traditional approaches to the task of ACE event extraction primarily rely on elabo- rately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack gener- alization, take a large amount of human effort and are prone to error propaga- tion and data sparsity problems. This paper proposes …...

Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by infer- ring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop re- lational path treated as an atomic …...

We present paired learning and inference algorithms for significantly reducing com- putation and increasing speed of the vector dot products in the classifiers that are at the heart of many NLP components. This is accomplished by partitioning the features into a sequence of templates which are or- dered such that …...

Vector space representation of words has been widely used to capture fine-grained linguistic regularities, and proven to be successful in various natural language pro- cessing tasks in recent years. However, existing models for learning word repre- sentations focus on either syntagmatic or paradigmatic relations alone. In this pa- per, we …...

Entity classification, like many other important problems in NLP, involves learning classifiers over sparse high- dimensional feature spaces that result from the conjunction of elementary fea- tures of the entity mention and its context. In this paper we develop a low-rank reg- ularization framework for training max- entropy models in …...

Online debate forums present a valu- able opportunity for the understanding and modeling of dialogue. To understand these debates, a key challenge is inferring the stances of the participants, all of which are interrelated and dependent. While collectively modeling users’ stances has been shown to be effective (Walker et al., …...

Contrasting meaning is a basic aspect of semantics. Recent word-embedding mod- els based on distributional semantics hy- pothesis are known to be weak for mod- eling lexical contrast. We present in this paper the embedding models that achieve an F-score of 92% on the widely-used, publicly available dataset, the GRE …...

Word embeddings have recently gained considerable popularity for modeling words in different Natural Language Processing (NLP) tasks including seman- tic similarity measurement. However, notwithstanding their success, word embeddings are by their very nature unable to capture polysemy, as different meanings of a word are conflated into a single representation. In …...

This paper considers the problem of em- bedding Knowledge Graphs (KGs) con- sisting of entities and relations into low- dimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only re- quirement is that the learned embeddings should be compatible within each individ- …...

Massive open online courses (MOOCs) are redefining the education system and transcending boundaries posed by tradi- tional courses. With the increase in pop- ularity of online courses, there is a cor- responding increase in the need to under- stand and interpret the communications of the course participants. Identifying top- ics …...

We present MultiGranCNN, a general deep learning architecture for matching text chunks. MultiGranCNN supports multigranular comparability of represen- tations: shorter sequences in one chunk can be directly compared to longer se- quences in the other chunk. Multi- GranCNN also contains a flexible and modularized match feature component that is easily …...

The ability to map descriptions of scenes to 3D geometric representations has many applications in areas such as art, educa- tion, and robotics. However, prior work on the text to 3D scene generation task has used manually specified object cate- gories and language that identifies them. We introduce a dataset …...

The Visual Dependency Representation (VDR) is an explicit model of the spa- tial relationships between objects in an im- age. In this paper we present an approach to training a VDR Parsing Model without the extensive human supervision used in previous work. Our approach is to find the objects mentioned …...

We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non- local translation phenomena. Second, we augment the architecture of the neural net- work with …...

The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target lan- guage model with a heuristically cho- sen source context window, achieving state-of-the-art performance in SMT. In this paper, we give a more sys- tematic treatment by summarizing the relevant source information through a …...

Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are com- parable to traditional approaches. A sig- nificant weakness in conventional NMT systems is their inability to correctly trans- late very rare words: end-to-end NMTs tend to have relatively small vocabularies with …...

Neural machine translation, a recently proposed approach to machine transla- tion based purely on neural networks, has shown promising results compared to the existing approaches such as phrase- based statistical machine translation. De- spite its recent success, neural machine translation has its limitation in handling a larger vocabulary, as training …...

We present a new algorithm for transforming dependency parse trees into phrase-structure parse trees. We cast the problem as struc- tured prediction and learn a statistical model. Our algorithm is faster than traditional phrase- structure parsing and achieves 90.4% English parsing accuracy and 82.4% Chinese parsing accuracy, near to the …...

Some state-of-the-art summarization systems use integer linear programming (ILP) based methods that aim to maximize the important concepts covered in the summary. These con- cepts are often obtained by selecting bigrams from the documents. In this paper, we improve such bigram based ILP summarization meth- ods from different aspects. First …...

When text is translated from one language into another, sentiment is preserved to varying degrees. In this paper, we use Arabic social media posts as stand-in for source language text, and determine loss in sentiment pre- dictability when they are translated into En- glish, manually and automatically. As bench- marks, …...