Articles

In recent years, with the development of Chi- nese semantically annotated corpus, such as Chinese Proposition Bank and Normalization Bank, the Chinese semantic role labeling (SRL) task has been boosted. Similar to Eng- lish, the Chinese SRL can be divided into two tasks: semantic role identification (SRI) and classification (SRC). …...

Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic- oriented, informative multi-document summa- rization. In this paper, we have experimented with one empirical and two unsupervised statistical machine learning techniques: k- means and Expectation Maximization (EM), for computing relative importance …...

In recent years there has been substantial work on the important problem of coreference res- olution, most of which has concentrated on the development of new models and algo- rithmic techniques. These works often show that complex models improve over a weak pairwise baseline. However, less attention has been given …...

Compared to the telephone, email based cus- tomer care is increasingly becoming the pre- ferred channel of communication for corpora- tions and customers. Most email-based cus- tomer care management systems provide a method to include template texts in order to re- duce the handling time for a customer’s email. The …...

This paper describes a language-independent, scalable system for both challenges of cross- document co-reference: name variation and entity disambiguation. We provide system re- sults from the ACE 2008 evaluation in both English and Arabic. Our English system’s ac- curacy is 8.4% relative better than an exact match baseline (and 14.2% …...

The Named Entity Recognition (NER) task has been garnering significant attention in NLP as it helps improve the performance of many natural language processing applica- tions. In this paper, we investigate the im- pact of using different sets of features in two discriminative machine learning frameworks, namely, Support Vector Machines …...

Human linguistic annotation is crucial for many natural language processing tasks but can be expensive and time-consuming. We ex- plore the use of Amazon’s Mechanical Turk system, a significantly cheaper and faster method for collecting annotations from a broad base of paid non-expert contributors over the Web. We investigate five …...

One major bottleneck in conversational sys- tems is their incapability in interpreting un- expected user language inputs such as out-of- vocabulary words. To overcome this problem, conversational systems must be able to learn new words automatically during human ma- chine conversation. Motivated by psycholin- guistic findings on eye gaze and …...

This paper describes a novel Bayesian ap- proach to unsupervised topic segmentation. Unsupervised systems for this task are driven by lexical cohesion: the tendency of well- formed segments to induce a compact and consistent lexical distribution. We show that lexical cohesion can be placed in a Bayesian context by modeling …...

Language comprehension, as with all other cases of the extraction of meaningful struc- ture from perceptual input, takes places un- der noisy conditions. If human language comprehension is a rational process in the sense of making use of all available infor- mation sources, then we might expect uncer- tainty at …...

Minimum-error-rate training (MERT) is a bot- tleneck for current development in statistical machine translation because it is limited in the number of weights it can reliably opti- mize. Building on the work of Watanabe et al., we explore the use of the MIRA algorithm of Crammer et al. as an …...

We advance the state-of-the-art for discrimi- natively trained machine translation systems by presenting novel probabilistic inference and search methods for synchronous gram- mars. By approximating the intractable space of all candidate translations produced by inter- secting an ngram language model with a synchronous grammar, we are able to train and …...

Translation rule extraction is a fundamental problem in machine translation, especially for linguistically syntax-based systems that need parse trees from either or both sides of the bi- text. The current dominant practice only uses 1-best trees, which adversely affects the rule set quality due to parsing errors. So we pro- …...

We improve the quality of paraphrases ex- tracted from parallel corpora by requiring that phrases and their paraphrases be the same syn- tactic type. This is achieved by parsing the En- glish side of a parallel corpus and altering the phrase extraction algorithm to extract phrase labels alongside bilingual phrase …...

We combine lexical, syntactic, and discourse features to produce a highly predictive model of human readers’ judgments of text readabil- ity. This is the first study to take into ac- count such a variety of linguistic factors and the first to empirically demonstrate that dis- course relations are strongly associated …...

We present a novel unsupervised sentence fu- sion method which we apply to a corpus of bi- ographies in German. Given a group of related sentences, we align their dependency trees and build a dependency graph. Using integer lin- ear programming we compress this graph to a new tree, which …...

We explore a stacked framework for learn- ing to predict dependency structures for natu- ral language sentences. A typical approach in graph-based dependency parsing has been to assume a factorized model, where local fea- tures are used but a global function is opti- mized (McDonald et al., 2005b). Recently Nivre …...

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and em- pirically efficient. Even with second-order features …...

Although research in other languages is in- creasing, much of the work in subjectivity analysis has been applied to English data, mainly due to the large body of electronic re- sources and tools that are available for this lan- guage. In this paper, we propose and evalu- ate methods that …...

In this paper, we present a novel method based on CRFs in response to the two special characteristics of “contextual dependency” and “label redundancy” in sentence sentiment classification. We try to capture the contextual constraints on sentence sentiment using CRFs. Through introducing redundant labels into the original sentimental label set …...