Articles

Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most bene- fit in the creation of a classifier. AL allows to learn accurate systems with much less anno- tated data than what is required by pure super- vised learning algorithms, …...

Stress is a useful cue for English word segmentation. A wide range of computa- tional models have found that stress cues enable a 2-10% improvement in segmen- tation accuracy, depending on the kind of model, by using input that has been anno- tated with stress using a pronouncing dic- tionary. …...

State-of-the-art fact extraction is heavily constrained by recall, as demonstrated by recent performance in TAC Slot Filling. We isolate this recall loss for NE slots by systematically analysing each stage of the slot filling pipeline as a filter over correct answers. Recall is critical as candidates never generated can never …...

Several state-of-the-art event extraction sys- tems employ models based on Support Vec- tor Machines (SVMs) in a pipeline architec- ture, which fails to exploit the joint depen- dencies that typically exist among events and arguments. While there have been at- tempts to overcome this limitation using Markov Logic Networks (MLNs), …...

Taxonomies are the backbone of many structured, semantic knowledge resources. Recent works for extracting taxonomic relations from text focused on collect- ing lexical-syntactic patterns to extract the taxonomic relations by matching the pat- terns to text. These approaches, however, often show low coverage due to the lack of contextual analysis …...

We propose a neural network approach to benefit from the non-linearity of corpus- wide statistics for part-of-speech (POS) tagging. We investigated several types of corpus-wide information for the words, such as word embeddings and POS tag dis- tributions. Since these statistics are en- coded as dense continuous features, it is …...

We present sentence enhancement as a novel technique for text-to-text genera- tion in abstractive summarization. Com- pared to extraction or previous approaches to sentence fusion, sentence enhancement increases the range of possible summary sentences by allowing the combination of dependency subtrees from any sentence from the source text. Our experiments …...

In this paper we introduce a new game to crowd-source natural language referring expressions. By designing a two player game, we can both collect and verify refer- ring expressions directly within the game. To date, the game has produced a dataset containing 130,525 expressions, referring to 96,654 distinct objects, in …...

Supervised methods have been the domi- nant approach for Chinese word segmen- tation. The performance can drop signif- icantly when the test domain is different from the training domain. In this paper, we study the problem of obtaining par- tial annotation from freely available data to help Chinese word segmentation …...

In this paper, we propose a joint model for unsupervised Chinese word segmentation (CWS). Inspired by the “products of ex- perts” idea, our joint model firstly com- bines two generative models, which are word-based hierarchical Dirichlet process model and character-based hidden Markov model, by simply multiplying their proba- bilities together. …...

We propose an unsupervised approach to constructing templates from a large collec- tion of semantic category names, and use the templates as the semantic representa- tion of categories. The main challenge is that many terms have multiple meanings, resulting in a lot of wrong templates. Sta- tistical data and semantic …...

State-of-the-art Chinese zero pronoun res- olution systems are supervised, thus re- lying on training data containing manu- ally resolved zero pronouns. To elimi- nate the reliance on annotated data, we present a generative model for unsuper- vised Chinese zero pronoun resolution. At the core of our model is a novel …...

Recent years have seen a surge of interest in stance classification in online debates. Oftentimes, however, it is important to de- termine not only the stance expressed by an author in her debate posts, but also the reasons behind her supporting or oppos- ing the issue under debate. We therefore …...

Almost all current dependency parsers classify based on millions of sparse indi- cator features. Not only do these features generalize poorly, but the cost of feature computation restricts parsing speed signif- icantly. In this work, we propose a novel way of learning a neural network classifier for use in a …...

Recurrent neural networks (RNNs) are con- nectionist models of sequential data that are naturally applicable to the analysis of natural language. Recently, “depth in space” — as an orthogonal notion to “depth in time” — in RNNs has been investigated by stacking mul- tiple layers of RNNs and shown empirically …...

We propose the first implementation of an infinite-order generative dependency model. The model is based on a new recursive neural network architecture, the Inside-Outside Recursive Neural Network. This architecture allows information to flow not only bottom-up, as in traditional recursive neural networks, but also top- down. This is achieved by …...

In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summariza- tion. We propose an innovative sentence compression method by considering every node in the constituent parse tree and de- ciding its status – remove or retain. In- teger liner programming with discrimina- tive …...

In this study, we analyzed the effects of ap- plying different levels of stemming approaches such as fixed-length word truncation and mor- phological analysis for multi-document sum- marization (MDS) on Turkish, which is an ag- glutinative and morphologically rich language. We constructed a manually annotated MDS data set, and to …...

This paper explores alternate algorithms, reward functions and feature sets for per- forming multi-document summarization using reinforcement learning with a high focus on reproducibility. We show that ROUGE results can be improved using a unigram and bigram similarity metric when training a learner to select sentences for summarization. Learners are …...

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We de- scribe our 13 summarization methods each from one of four summarization strate- gies. We show how well our methods perform using Farsi text from the CLEF 2008 shared-task, …...