Articles

Recent work on bilingual Word Sense Disam- biguation (WSD) has shown that a resource deprived language (L1) can benefit from the annotation work done in a resource rich lan- guage (L2) via parameter projection. How- ever, this method assumes the presence of suf- ficient annotated data in one resource rich …...

In this paper, we observe that there exists a second dimension to the relation extraction (RE) problem that is orthogonal to the relation type dimension. We show that most of these second dimensional structures are relatively constrained and not difficult to identify. We propose a novel algorithmic approach to RE …...

Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web’s natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors. Recently, researchers have …...

We design a class of submodular functions meant for document summarization tasks. These functions each combine two terms, one which encourages the summary to be representative of the corpus, and the other which positively rewards diversity. Critically, our functions are monotone nondecreasing and submodular, which means that an efficient scalable …...

We present a simple semi-supervised relation extraction system with large-scale word clustering. We focus on systematically exploring the effectiveness of different cluster-based features. We also propose several statistical methods for selecting clusters at an appropriate level of granularity. When training on different sizes of data, our semi-supervised approach consistently outperformed …...

In citation-based summarization, text written by several researchers is leveraged to identify the important aspects of a target paper. Previ- ous work on this problem focused almost ex- clusively on its extraction aspect (i.e. selecting a representative set of citation sentences that highlight the contribution of the target paper). Meanwhile, …...

Extractive methods for multi-document sum- marization are mainly governed by informa- tion overlap, coherence, and content con- straints. We present an unsupervised proba- bilistic approach to model the hidden abstract concepts across documents as well as the cor- relation between these concepts, to generate topically coherent and non-redundant sum- maries. …...

Via an oracle experiment, we show that the upper bound on accuracy of a CCG parser is significantly lowered when its search space is pruned using a supertagger, though the su- pertagger also prunes many bad parses. In- spired by this analysis, we design a single model with both supertagging …...

We learn a joint model of sentence extraction and compression for multi-document summa- rization. Our model scores candidate sum- maries according to a combined linear model whose features factor over (1) the n-gram types in the summary and (2) the compres- sions used. We train the model using a margin- …...

We present a method for the computation of prefix probabilities for synchronous context- free grammars. Our framework is fairly gen- eral and relies on the combination of a sim- ple, novel grammar transformation and stan- dard techniques to bring grammars into nor- mal forms. 1...

We present a novel approach to discovering re- lations and their instantiations from a collec- tion of documents in a single domain. Our approach learns relation types by exploiting meta-constraints that characterize the general qualities of a good relation in any domain. These constraints state that instances of a single …...

We study the problem of finding the best head- driven parsing strategy for Linear Context- Free Rewriting System productions. A head- driven strategy must begin with a specified righthand-side nonterminal (the head) and add the remaining nonterminals one at a time in any order. We show that it is NP-hard …...

We propose a language-independent method for the automatic extraction of transliteration pairs from parallel corpora. In contrast to previous work, our method uses no form of supervision, and does not require linguisti- cally informed preprocessing. We conduct experiments on data sets from the NEWS 2010 shared task on transliteration mining …...

Efficient decoding for syntactic parsing has become a necessary research area as statisti- cal grammars grow in accuracy and size and as more NLP applications leverage syntac- tic analyses. We review prior methods for pruning and then present a new framework that unifies their strengths into a single ap- proach. …...

Unsupervised word alignment is most often modeled as a Markov process that generates a sentence f conditioned on its translation e. A similar model generating e from f will make different alignment predictions. Statistical machine translation systems combine the pre- dictions of two directional models, typically using heuristic combination procedures …...

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text messages and propose a method for identify- ing and normalising ill-formed words. Our method uses a classifier to detect ill-formed …...

We present a probabilistic topic model for jointly identifying properties and attributes of social media review snippets. Our model simultaneously learns a set of properties of a product and captures aggregate user senti- ments towards these properties. This approach directly enables discovery of highly rated or inconsistent properties of a …...

We present disputant relation-based meth- od for classifying news articles on conten- tious issues. We observe that the disputants of a contention are an important feature for understanding the discourse. It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the …...

This paper presents a pilot study of opinion summarization on conversations. We create a corpus containing extractive and abstrac- tive summaries of speaker’s opinion towards a given topic using 88 telephone conversa- tions. We adopt two methods to perform ex- tractive summarization. The first one is a sentence-ranking method that …...

The challenges of Named Entities Recogni- tion (NER) for tweets lie in the insufficient information in a tweet and the unavailabil- ity of training data. We propose to com- bine a K-Nearest Neighbors (KNN) classi- fier with a linear Conditional Random Fields (CRF) model under a semi-supervised learn- ing framework …...