Articles

We propose a method for annotating post- to-post discourse structure in online user forum data, in the hopes of improving troubleshooting-oriented information ac- cess. We introduce the tasks of: (1) post classification, based on a novel dialogue act tag set; and (2) link classification. We also introduce three feature sets …...

We present a simple technique for learn- ing better SVMs using fewer training ex- amples. Rather than using the standard SVM regularization, we regularize toward low weight-variance. Our new SVM ob- jective remains a convex quadratic func- tion of the weights, and is therefore com- putationally no harder to optimize …...

Recent work in computer vision has aimed to associate image regions with keywords describing the depicted entities, but ac- tual image ‘understanding’ would also re- quire identifying their attributes, relations and activities. Since this information can- not be conveyed by simple keywords, we have collected a corpus of “action” photos …...

This paper presents a comparative study of three closely related Bayesian mod- els for unsupervised document level senti- ment classification, namely, the latent sen- timent model (LSM), the joint sentiment- topic (JST) model, and the Reverse-JST model. Extensive experiments have been conducted on two corpora, the movie re- view dataset …...

In this paper, the authors present a new ap- proach to sentence level sentiment analysis. The aim is to determine whether a sentence expresses a positive, negative or neutral sen- timent, as well as its intensity. The method performs WSD over the words in the sentence in order to work …...

Supervised learning has been recently used to improve the performance of word alignment. However, due to the limited amount of labeled data, the performance of ”pure” supervised learning, which only used labeled data, is limited. As a re- sult, many existing methods employ fea- tures learnt from a large amount …...

The availability of substantial, in-domain parallel corpora is critical for the develop- ment of high-performance statistical ma- chine translation (SMT) systems. Such corpora, however, are expensive to pro- duce due to the labor intensive nature of manual translation. We propose to al- leviate this problem with a novel, semi- supervised, …...

Sarcasm is a form of speech act in which the speakers convey their message in an implicit way. The inherently ambiguous nature of sarcasm sometimes makes it hard even for humans to decide whether an ut- terance is sarcastic or not. Recognition of sarcasm can benefit many sentiment analy- sis …...

Prior work on training the IBM-3 transla- tion model is based on suboptimal meth- ods for computing Viterbi alignments. In this paper, we present the first method guaranteed to produce globally optimal alignments. This not only results in im- proved alignments, it also gives us the op- portunity to evaluate …...

Probabilistic phrase-based synchronous grammars are now considered promis- ing devices for statistical machine transla- tion because they can express reordering phenomena between pairs of languages. Learning these hierarchical, probabilistic devices from parallel corpora constitutes a major challenge, because of multiple la- tent model variables as well as the risk of …...

In this paper we present a cognitively plau- sible approach to word segmentation that segments in an online fashion using only local information and a lexicon of pre- viously segmented words. Unlike popu- lar statistical optimization techniques, the learner uses structural information of the input syllables rather than distributional cues …...

Clustering is a central technique in NLP. Consequently, clustering evaluation is of great importance. Many clustering algo- rithms are evaluated by their success in tagging corpus tokens. In this paper we discuss type level evaluation, which re- flects class membership only and is inde- pendent of the token statistics of …...

We demonstrate that relational features derived from dependency-syntactic and semantic role structures are useful for the task of detecting opinionated expressions in natural-language text, significantly im- proving over conventional models based on sequence labeling with local features. These features allow us to model the way opinionated expressions interact in a …...

This work shows how to improve state-of-the-art monolingual natural language processing models using unannotated bilingual text. We build a mul- tiview learning objective that enforces agreement between monolingual and bilingual models. In our method the first, monolingual view consists of supervised predictors learned separately for each language. The second, bilingual …...

This paper describes a new method for un- supervised grammar induction based on the automatic extraction of certain pat- terns in the texts. Our starting hypoth- esis is that there exist some classes of words that function as separators, mark- ing the beginning or the end of new con- stituents. …...

A central problem for NLP is grammar in- duction: the development of unsupervised learning algorithms for syntax. In this pa- per we present a lattice-theoretic represen- tation for natural language syntax, called Distributional Lattice Grammars. These representations are objective or empiri- cist, based on a generalisation of distribu- tional learning, …...

We show that Viterbi (or “hard”) EM is well-suited to unsupervised grammar in- duction. It is more accurate than standard inside-outside re-estimation (classic EM), significantly faster, and simpler. Our ex- periments with Klein and Manning’s De- pendency Model with Valence (DMV) at- tain state-of-the-art performance — 44.8% accuracy on Section …...

Current approaches to semantic parsing, the task of converting text to a formal meaning representation, rely on annotated training data mapping sentences to logi- cal forms. Providing this supervision is a major bottleneck in scaling semantic parsers. This paper presents a new learn- ing paradigm aimed at alleviating the su- …...

Modern unsupervised POS taggers usually apply an optimization procedure to a non- convex function, and tend to converge to local maxima that are sensitive to start- ing conditions. The quality of the tag- ging induced by such algorithms is thus highly variable, and researchers report av- erage results over several …...

This paper presents an algorithm for unsu- pervised co-occurrence based parsing that improves and extends existing approaches. The proposed algorithm induces a context- free grammar of the language in question in an iterative manner. The resulting struc- ture of a sentence will be given as a hier- archical arrangement of …...