Articles

Latent variable models have the potential to add value to large document collections by discovering interpretable, low-dimensional subspaces. In order for people to use such models, however, they must trust them. Un- fortunately, typical dimensionality reduction methods for text, such as latent Dirichlet al- location, often produce low-dimensional sub- spaces …...

Argumentative Zoning (AZ) – analysis of the argumentative structure of a scientific paper – has proved useful for a number of informa- tion access tasks. Current approaches to AZ rely on supervised machine learning (ML). Requiring large amounts of annotated data, these approaches are expensive to develop and port to …...

Dual decomposition has been recently pro- posed as a way of combining complemen- tary models, with a boost in predictive power. However, in cases where lightweight decom- positions are not readily available (e.g., due to the presence of rich features or logical con- straints), the original subgradient algorithm is inefficient. …...

We exploit sketch techniques, especially the Count-Min sketch, a memory, and time effi- cient framework which approximates the fre- quency of a word pair in the corpus without explicitly storing the word pair itself. These methods use hashing to deal with massive amounts of streaming text. We apply Count- Min …...

We propose a simple training regime that can improve the extrinsic performance of a parser, given only a corpus of sentences and a way to automatically evaluate the extrinsic quality of a candidate parse. We apply our method to train parsers that excel when used as part of a reordering …...

When translating among languages that differ substantially in word order, machine transla- tion (MT) systems benefit from syntactic pre- ordering—an approach that uses features from a syntactic parse to permute source words into a target-language-like order. This paper presents a method for inducing parse trees au- tomatically from a parallel …...

We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. Thus, we can model the compositional effects required for accu- rate assignment of phrase-level sentiment. For example, combining an adverb (e.g., “very”) with a positive polar adjective (e.g., …...

Polarity classification of opinionated sen- tences with both positive and negative senti- ments1 is a key challenge in sentiment anal- ysis. This paper presents a novel unsuper- vised method for discovering intra-sentence level discourse relations for eliminating polar- ity ambiguities. Firstly, a discourse scheme with discourse constraints on polarity was …...

We introduce a novel machine learning frame- work based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space representations for multi-word phrases. In sentiment prediction tasks these represen- tations outperform other state-of-the-art ap- proaches on commonly used datasets, such as movie reviews, without using …...

This paper presents a domain-assisted ap- proach to organize various aspects of a prod- uct into a hierarchy by integrating domain knowledge (e.g., the product specifications), as well as consumer reviews. Based on the derived hierarchy, we generate a hierarchical organization of consumer reviews on various product aspects and aggregate …...

This paper presents a model that extends se- mantic role labeling. Existing approaches in- dependently analyze relations expressed by verb predicates or those expressed as nominal- izations. However, sentences express relations via other linguistic phenomena as well. Fur- thermore, these phenomena interact with each other, thus restricting the structures they …...

Class-instance label propagation algorithms have been successfully used to fuse informa- tion from multiple sources in order to enrich a set of unlabeled instances with class labels. Yet, nobody has explored the relationships be- tween the instances themselves to enhance an initial set of class-instance pairs. We pro- pose two …...

We present a novel approach for automatic collocation error correction in learner English which is based on paraphrases extracted from parallel corpora. Our key assumption is that collocation errors are often caused by se- mantic similarity in the first language (L1- language) of the writer. An analysis of a large …...

We present a novel approach to Data-Oriented Parsing (DOP). Like other DOP models, our parser utilizes syntactic fragments of arbitrary size from a treebank to analyze new sentences, but, crucially, it uses only those which are encountered at least twice. This criterion al- lows us to work with a relatively …...

We present a method that paraphrases a given sentence by first generating candidate para- phrases and then ranking (or classifying) them. The candidates are generated by ap- plying existing paraphrasing rules extracted from parallel corpora. The ranking compo- nent considers not only the overall quality of the rules that produced …...

We propose a method to improve the accuracy of parsing bilingual texts (bitexts) with the help of statistical machine translation (SMT) systems. Previous bitext parsing methods use human-annotated bilingual treebanks that are hard to obtain. Instead, our approach uses an auto-generated bilingual treebank to produce bilingual constraints. However, because the …...

We present a simple method for transferring dependency parsers from source languages with labeled training data to target languages without labeled training data. We first demon- strate that delexicalized parsers can be di- rectly transferred between languages, produc- ing significantly higher accuracies than unsu- pervised parsers. We then use a …...

We describe a method for prediction of lin- guistic structure in a language for which only unlabeled data is available, using annotated data from a set of one or more helper lan- guages. Our approach is based on a model that locally mixes between supervised mod- els from the helper …...

Minimum error rate training is a crucial compo- nent to many state-of-the-art NLP applications, such as machine translation and speech recog- nition. However, common evaluation functions such as BLEU or word error rate are generally highly non-convex and thus prone to search errors. In this paper, we present LP-MERT, an …...

This paper describes an algorithm for exact decoding of phrase-based translation models, based on Lagrangian relaxation. The method recovers exact solutions, with certificates of optimality, on over 99% of test examples. The method is much more efficient than ap- proaches based on linear programming (LP) or integer linear programming (ILP) …...