Articles

Prepositional phrases (PPs) express cru- cial information that knowledge base con- struction methods need to extract. How- ever, PPs are a major source of syntactic ambiguity and still pose problems in pars- ing. We present a method for resolving ambiguities arising from PPs, making ex- tensive use of semantic knowledge …...

A standard pipeline for statistical rela- tional learning involves two steps: one first constructs the knowledge base (KB) from text, and then performs the learn- ing and reasoning tasks using probabilis- tic first-order logics. However, a key is- sue is that information extraction (IE) er- rors from text affect the …...

Automatic speech recognition (ASR) out- puts often contain various disfluencies. It is necessary to remove these disfluencies before processing downstream tasks. In this paper, an efficient disfluency detection approach based on right-to-left transition- based parsing is proposed, which can effi- ciently identify disfluencies and keep ASR outputs grammatical. Our method …...

Relation triples produced by open domain information extraction (open IE) systems are useful for question answering, infer- ence, and other IE tasks. Traditionally these are extracted using a large set of pat- terns; however, this approach is brittle on out-of-domain text and long-range depen- dencies, and gives no insight into …...

We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automat- ically parsed sentences. Given this fixed network representation, we learn a final layer using the struc- tured perceptron with beam-search decoding. On …...

We propose a technique for learning rep- resentations of parser states in transition- based dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networks— the stack LSTM. Like the conventional stack data structures used in transition- based parsing, elements can be pushed to or popped from …...

Most existing graph-based parsing models rely on millions of hand-crafted features, which limits their generalization ability and slows down the parsing speed. In this paper, we propose a general and effective Neural Network model for graph-based dependency parsing. Our model can auto- matically learn high-order feature combi- nations using only …...

This paper describes a parsing model that combines the exact dynamic programming of CRF parsing with the rich nonlinear fea- turization of neural net approaches. Our model is structurally a CRF that factors over anchored rule productions, but in- stead of linear potential functions based on sparse features, we use …...

Over the last two decades, numerous algo- rithms have been developed that success- fully capture something of the semantics of single words by looking at their distri- bution in text and comparing these distri- butions in a vector space model. How- ever, it is not straightforward to construct meaning representations …...

An elementary way of using language is to refer to objects. Often, these objects are physically present in the shared envi- ronment and reference is done via men- tion of perceivable properties of the ob- jects. This is a type of language use that is modelled well neither by logical …...

Zero-shot methods in language, vision and other domains rely on a cross-space map- ping function that projects vectors from the relevant feature space (e.g., visual- feature-based image representations) to a large semantic word space (induced in an unsupervised way from corpus data), where the entities of interest (e.g., objects images …...

Answering natural language questions over a knowledge base is an important and challenging task. Most of existing sys- tems typically rely on hand-crafted fea- tures and rules to conduct question under- standing and/or answer ranking. In this pa- per, we introduce multi-column convolu- tional neural networks (MCCNNs) to un- derstand …...

Community question answering (cQA) has become an important issue due to the popularity of cQA archives on the web. This paper is concerned with the problem of question retrieval. Question retrieval in cQA archives aims to find the exist- ing questions that are semantically equiv- alent or relevant to the …...

Understanding open-domain text is one of the primary challenges in NLP. Ma- chine comprehension evaluates the sys- tem’s ability to understand text through a series of question-answering tasks on short pieces of text such that the correct answer can be found only in the given text. For this task, we …...

Lexical selection is crucial for statistical ma- chine translation. Previous studies separately exploit sentence-level contexts and document- level topics for lexical selection, neglecting their correlations. In this paper, we propose a context-aware topic model for lexical selec- tion, which not only models local contexts and global topics but also captures …...

We present a method for predicting ma- chine translation output quality geared to the needs of computer-assisted translation. These include the capability to: i) con- tinuously learn and self-adapt to a stream of data coming from multiple translation jobs, ii) react to data diversity by ex- ploiting human feedback, and …...

Simultaneous translation is a method to reduce the latency of communication through machine translation (MT) by di- viding the input into short segments be- fore performing translation. However, short segments pose problems for syntax- based translation methods, as it is diffi- cult to generate accurate parse trees for sub-sentential segments. …...

We present an efficient incremental top- down parsing method for preordering based on Bracketing Transduction Gram- mar (BTG). The BTG-based preordering framework (Neubig et al., 2012) can be applied to any language using only par- allel text, but has the problem of compu- tational efficiency. Our top-down parsing algorithm allows …...

This paper presents a generative model to event schema induction. Previous meth- ods in the literature only use head words to represent entities. However, elements other than head words contain useful in- formation. For instance, an armed man is more discriminative than man. Our model takes into account this information …...

We present results on using stacking to en- semble multiple systems for the Knowl- edge Base Population English Slot Fill- ing (KBP-ESF) task. In addition to us- ing the output and confidence of each sys- tem as input to the stacked classifier, we also use features capturing how well the …...