Articles

The narrative cloze is an evaluation met- ric commonly used for work on automatic script induction. While prior work in this area has focused on count-based meth- ods from distributional semantics, such as pointwise mutual information, we argue that the narrative cloze can be productively reframed as a language modeling …...

A prerequisite relation describes a basic relation among concepts in cognition, ed- ucation and other areas. However, as a se- mantic relation, it has not been well stud- ied in computational linguistics. We inves- tigate the problem of measuring prereq- uisite relations among concepts and pro- pose a simple link-based …...

We consider the problem of embedding knowledge graphs (KGs) into continuous vector spaces. Existing methods can on- ly deal with explicit relationships within each triple, i.e., local connectivity pattern- s, but cannot handle implicit relationship- s across different triples, i.e., contextual connectivity patterns. This paper proposes context-dependent KG embedding, a …...

Events are communicated in natural lan- guage with varying degrees of certainty. For example, if you are “hoping for a raise,” it may be somewhat less likely than if you are “expecting” one. To study these distinctions, we present scalable, high- quality annotation schemes for event de- tection and fine-grained …...

We propose a novel method for acquiring entailment pairs of binary patterns on a large-scale. This method exploits the tran- sitivity of entailment and a self-training scheme to improve the performance of an already strong supervised classifier for en- tailment, and unlike previous methods that exploit transitivity, it works on …...

In this paper we analyze the perfor- mance of different composition models on a large dataset of German compound nouns. Given a vector space model for the German language, we try to reconstruct the observed representation (the corpus- estimated vector) of a compound by com- posing the observed representations of …...

Traditional approaches to Chinese Seman- tic Role Labeling (SRL) almost heavily re- ly on feature engineering. Even worse, the long-range dependencies in a sentence can hardly be modeled by these method- s. In this paper, we introduce bidirection- al recurrent neural network (RNN) with long-short-term memory (LSTM) to cap- ture …...

Recognizing Text Entailment (RTE) plays an important role in NLP applications in- cluding question answering, information retrieval, etc. In recent work, some re- search explore “deep” expressions such as discourse commitments or strict logic for representing the text. However, these ex- pressions suffer from the limitation of in- ference inconvenience …...

Due to the commonality in natural language, negation focus plays a critical role in deep understanding of context. However, existing studies for negation focus identification ma- jor on supervised learning which is time- consuming and expensive due to manual preparation of annotated corpus. To address this problem, we propose an …...

Modeling non-stationary time-series data for making predictions is a challenging but important task. One of the key is- sues is to identify long-term changes accu- rately in time-varying data. Bayesian On- line Change Point Detection (BO-CPD) algorithms efficiently detect long-term changes without assuming the Markov property which is vulnerable to …...

Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)- based methods. However some mod- els are opaque to probabilistic interpre- tation, and MF-based methods, typically solved using Singular Value Decomposi- tion (SVD), may incur loss of corpus in- formation. In addition, it is …...

Modeling sentence similarity is compli- cated by the ambiguity and variability of linguistic expression. To cope with these challenges, we propose a model for com- paring sentences that uses a multiplicity of perspectives. We first model each sentence using a convolutional neural network that extracts features at multiple levels of …...

The success of deep learning often de- rives from well-chosen operational build- ing blocks. In this work, we revise the temporal convolution operation in CNNs to better adapt it to text processing. In- stead of concatenating word representa- tions, we appeal to tensor algebra and use low-rank n-gram tensors to …...

Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models. This paper introduces two probabilistic topic models, Correlated LDA (C-LDA) and Correlated HDP (C- HDP). These address problems that can arise when analyzing large, asymmetric, and potentially weakly-related collections. …...

Deep compositional models of meaning acting on distributional representations of words in order to produce vectors of larger text constituents are evolving to a pop- ular area of NLP research. We detail a compositional distributional framework based on a rich form of word embeddings that aims at facilitating the interactions …...

We introduce a model for construct- ing vector representations of words by composing characters using bidirectional LSTMs. Relative to traditional word rep- resentation models that have independent vectors for each word type, our model requires only a single vector per char- acter type and a fixed set of parame- ters …...

Authoring a scientific paper is a complex process involving many decisions. We in- troduce a probabilistic model of some of the important aspects of that process: that authors have individual preferences, that writing a paper requires trading off among the preferences of authors as well as ex- trinsic rewards in …...

Many models in natural language process- ing define probabilistic distributions over linguistic structures. We argue that (1) the quality of a model’s posterior distribu- tion can and should be directly evaluated, as to whether probabilities correspond to empirical frequencies; and (2) NLP uncer- tainty can be projected not only to …...

Models that learn to represent textual and knowledge base relations in the same con- tinuous latent space are able to perform joint inferences among the two kinds of re- lations and obtain high accuracy on knowl- edge base completion (Riedel et al., 2013). In this paper we propose a model …...

We explore some of the practicalities of using random walk inference meth- ods, such as the Path Ranking Algorithm (PRA), for the task of knowledge base completion. We show that the random walk probabilities computed (at great ex- pense) by PRA provide no discernible benefit to performance on this task, …...