Articles

The current state-of-the-art single- document summarization method gen- erates a summary by solving a Tree Knapsack Problem (TKP), which is the problem of finding the optimal rooted sub- tree of the dependency-based discourse tree (DEP-DT) of a document. We can obtain a gold DEP-DT by transforming a gold Rhetorical Structure …...

In this paper, we propose a new frame- work that unifies the output of three infor- mation extraction (IE) tasks - entity men- tions, relations and events as an informa- tion network representation, and extracts all of them using one single joint model based on structured prediction. This novel formulation …...

Temporal scope adds a time dimension to facts in Knowledge Bases (KBs). These time scopes specify the time periods when a given fact was valid in real life. With- out temporal scope, many facts are under- specified, reducing the usefulness of the data for upper level applications such as Question …...

In this paper, we provide a new method for decoding tree transduction based sentence compression models augmented with lan- guage model scores, by jointly decoding two components. In our proposed so- lution, rich local discriminative features can be easily integrated without increasing computational complexity. Utilizing an unobvious fact that the …...

Multi-label text categorization (MTC) is supervised learning, where a documen- t may be assigned with multiple categories (labels) simultaneously. The labels in the MTC are correlated and the correlation re- sults in some hidden components, which represent the ”share” variance of correlat- ed labels. In this paper, we propose a …...

We describe a convolutional neural net- work that learns feature representations for short textual posts using hashtags as a su- pervised signal. The proposed approach is trained on up to 5.5 billion words predict- ing 100,000 possible hashtags. As well as strong performance on the hashtag predic- tion task itself, …...

We develop a statistical model of saccadic eye movements during reading of isolated sentences. The model is focused on rep- resenting individual differences between readers and supports the inference of the most likely reader for a novel set of eye movement patterns. We empirically study the model for biometric reader …...

Previous work on extracting ideology from text has focused on domains where expression of political views is expected, but it’s unclear if current technology can work in domains where displays of ide- ology are considered inappropriate. We present a supervised ensemble n-gram model for ideology extraction with topic adjustments and …...

We propose a model for jointly predicting multiple emotions in natural language sen- tences. Our model is based on a low-rank coregionalisation approach, which com- bines a vector-valued Gaussian Process with a rich parameterisation scheme. We show that our approach is able to learn correlations and anti-correlations between emotions on …...

We propose a simple unsupervised ap- proach to detecting non-compositional components in multiword expressions based on Wiktionary. The approach makes use of the definitions, synonyms and trans- lations in Wiktionary, and is applicable to any type of MWE in any language, assum- ing the MWE is contained in Wiktionary. Our …...

We introduce new features for incorpo- rating semantic predicate-argument struc- tures in machine translation (MT). The methods focus on the completeness of the semantic structures of the translations, as well as the order of the translated seman- tic roles. We experiment with translation rules which contain the core arguments for …...

Previous work often used a pipelined framework where Chinese word segmen- tation is followed by term extraction and keyword extraction. Such framework suf- fers from error propagation and is un- able to leverage information in later mod- ules for prior components. In this paper, we propose a four-level Dirichlet Process …...

Manual analysis and decryption of enci- phered documents is a tedious and error prone work. Often—even after spend- ing large amounts of time on a par- ticular cipher—no decipherment can be found. Automating the decryption of var- ious types of ciphers makes it possible to sift through the large number …...

In this paper, we present two improve- ments to the beam search approach for solving homophonic substitution ciphers presented in Nuhn et al. (2013): An im- proved rest cost estimation together with an optimized strategy for obtaining the or- der in which the symbols of the cipher are deciphered reduces …...

Phrase reordering is a challenge for statis- tical machine translation systems. Posing phrase movements as a prediction prob- lem using contextual features modeled by maximum entropy-based classifier is su- perior to the commonly used lexicalized reordering model. However, Training this discriminative model using large-scale parallel corpus might be computationally expensive. …...

Markov chain Monte Carlo (MCMC) approxi- mates the posterior distribution of latent vari- able models by generating many samples and averaging over them. In practice, however, it is often more convenient to cut corners, using only a single sample or following a suboptimal averaging strategy. We systematically study dif- ferent …...

This paper applies type-based Markov Chain Monte Carlo (MCMC) algorithms to the problem of learning Synchronous Context-Free Grammar (SCFG) rules from a forest that represents all possible rules consistent with a fixed word align- ment. While type-based MCMC has been shown to be effective in a number of NLP applications, …...

Vector space models (VSMs) are math- ematically well-defined frameworks that have been widely used in the distributional approaches to semantics. In VSMs, high- dimensional vectors represent linguistic entities. In an application, the similar- ity of vectors—and thus the entities that they represent—is computed by a distance formula. The high dimensionality …...

Automatically compiling bilingual dictio- naries of technical terms from comparable corpora is a challenging problem, yet with many potential applications. In this paper, we exploit two independent observations about term translations: (a) terms are of- ten formed by corresponding sub-lexical units across languages and (b) a term and its translation …...

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vec- tors for sentence-level classification tasks. We show that a simple CNN with lit- tle hyperparameter tuning and static vec- tors achieves excellent results on multi- ple benchmarks. Learning task-specific vectors through …...