Articles

Large databases of facts are prevalent in many applications. Such databases are accurate, but as they broaden their scope they become increasingly incomplete. In contrast to extending such a database, we present a system to query whether it con- tains an arbitrary fact. This work can be thought of as …...

Most compositional-distributional models of meaning are based on ambiguous vec- tor representations, where all the senses of a word are fused into the same vec- tor. This paper provides evidence that the addition of a vector disambiguation step prior to the actual composition would be beneficial to the whole process, …...

Determining the stance expressed by an author from a post written for a two-sided debate in an online debate forum is a relatively new problem in opinion min- ing. We extend a state-of-the-art learning- based approach to debate stance classifica- tion by (1) inducing lexico-syntactic pat- terns based on syntactic …...

Vector-space word representations have been very successful in recent years at im- proving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as in- dependent entities without any explicit re- lationship among morphologically related words being modeled. As a result, rare and complex …...

In this paper, we propose a new method for semantic class induction. First, we in- troduce a generative model of sentences, based on dependency trees and which takes into account homonymy. Our model can thus be seen as a generalization of Brown clustering. Second, we describe an efficient algorithm to …...

In most previous research on distribu- tional semantics, Vector Space Models (VSMs) of words are built either from topical information (e.g., documents in which a word is present), or from syntac- tic/semantic types of words (e.g., depen- dency parse links of a word in sentences), but not both. In this …...

This paper proposes passage reranking models that (i) do not require manual fea- ture engineering and (ii) greatly preserve accuracy, when changing application do- main. Their main characteristic is the use of relational semantic structures rep- resenting questions and their answer pas- sages. The relations are established us- ing information …...

Sentence compression techniques often assemble output sentences using frag- ments of lexical sequences such as n- grams or units of syntactic structure such as edges from a dependency tree repre- sentation. We present a novel approach for discriminative sentence compression that unifies these notions and jointly pro- duces sequential and …...

We derive a spectral algorithm for learn- ing the parameters of a refinement HMM. This method is simple, efficient, and can be applied to a wide range of supervised sequence labeling tasks. Like other spec- tral methods, it avoids the problem of lo- cal optima and provides a consistent esti- …...

We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, the surface forms of morphemes. Our focus is on a low- resource learning setting, in which only a small amount of annotated word forms are available for model training, while unan- notated word forms are available in …...

This paper proposes a boosting algorithm that uses a semi-Markov perceptron. The training algorithm repeats the training of a semi-Markov model and the update of the weights of training samples. In the boost- ing, training samples that are incorrectly segmented or labeled have large weights. Such training samples are aggressively …...

We design a new co-occurrence based word association measure by incorpo- rating the concept of significant co- occurrence in the popular word associ- ation measure Pointwise Mutual Infor- mation (PMI). By extensive experiments with a large number of publicly available datasets we show that the newly intro- duced measure performs …...

Within the natural language processing (NLP) community, active learning has been widely investigated and applied in or- der to alleviate the annotation bottleneck faced by developers of new NLP systems and technologies. This paper presents the first theoretical analysis of stopping active learning based on stabilizing predictions (SP). The analysis …...

Active learning and domain adaptation are both important tools for reducing labeling effort to learn a good supervised model in a target domain. In this paper, we inves- tigate the problem of online active learn- ing within a new active domain adapta- tion setting: there are insufficient labeled data in …...

We present a flexible formulation of semi- supervised learning for structured mod- els, which seamlessly incorporates graph- based and more general supervision by ex- tending the posterior regularization (PR) framework. Our extension allows for any regularizer that is a convex, differentiable function of the appropriate marginals. We show that surprisingly, …...

In this paper, we study the answer sentence selection problem for ques- tion answering. Unlike previous work, which primarily leverages syntactic analy- sis through dependency tree matching, we focus on improving the performance us- ing models of lexical semantic resources. Experiments show that our systems can be consistently and significantly …...

In Community question answering (QA) sites, malicious users may provide decep- tive answers to promote their products or services. It is important to identify and fil- ter out these deceptive answers. In this paper, we first solve this problem with the traditional supervised learning meth- ods. Two kinds of features, …...

Query segmentation, like text chunking, is the first step towards query understand- ing. In this study, we explore the effec- tiveness of crowdsourcing for this task. Through carefully designed control ex- periments and Inter Annotator Agreement metrics for analysis of experimental data, we show that crowdsourcing may not be a …...

This work proposes a new segmentation evaluation metric, named boundary simi- larity (B), an inter-coder agreement coef- ficient adaptation, and a confusion-matrix for segmentation that are all based upon an adaptation of the boundary edit distance in Fournier and Inkpen (2012). Existing seg- mentation metrics such as Pk, WindowD- iff, …...

Repeating experiments is an important in- strument in the scientific toolbox to vali- date previous work and build upon exist- ing work. We present two concrete use cases involving key techniques in the NLP domain for which we show that reproduc- ing results is still difficult. We show that the …...