Articles

Knowledge-based Word sense Disambiguation (WSD) methods heavily depend on knowledge. Therefore enriching knowledge is one of the most important issues in WSD. This paper proposes a novel idea of combining WordNet and ConceptNet for WSD. First, we present a novel method to automatically disambiguate the concepts in ConceptNet; and then …...

Lack of training data is one of the severest problems facing word sense disambigua- tion (WSD). To overcome the problem, we propose a method that combines automatic labeled data expansion (Step-1) and semi- supervised learning (Step-2). The Step-1 and 2 methods are both effective, but their combination has a synergistic …...

Several bilingual WSD algorithms which exploit translation correspondences be- tween parallel corpora have been pro- posed. However, the availability of such parallel corpora itself is a tall task for some of the resource constrained languages of the world. We propose an unsupervised bilingual EM based algorithm which relies on the …...

Phrase-based statistical machine transla- tion (PBSMT) decoders translate source sentences one phrase at a time using strong independence assumptions over the source phrases. Translation table scores are typ- ically independent of context, language model scores depend on a few words sur- rounding the target phrase and distortion models do not …...

Cross-adaptation (CA) based methods of machine translation (MT) system combi- nation work by adapting the decoding step of a baseline system using information from alternate systems. Generally, the re- quired information is very deep, such as a full decoding forest. In this paper, we de- scribe a method of cross-adaptation …...

The direct optimization of a translation metric is an integral part of building state- of-the-art SMT systems. Unfortunately, widely used translation metrics such as BLEU-score are non-smooth, non-convex, and non-trivial to optimize. Thus, stan- dard optimizers such as minimum error rate training (MERT) can be extremely time-consuming, leading to a …...

This paper investigates parameter adap- tation in Statistical Machine Transla- tion(SMT). To overcome the parameter bias-estimation problem with Minimum Error Rate Training(MERT), we extend it under a transductive learning framework, by iteratively re-estimating the parame- ters using both development and test da- ta, in which the translation hypotheses of the …...

The term “genre” covers different aspects of both texts and documents, and it has led to many classification schemes. This makes different approaches to genre iden- tification incomparable and the task itself unclear. We introduce the linguistically motivated text classification task language function analysis, LFA, which focuses on one well-defined …...

Classification of citations into categories such as use, refutation, comparison etc. may have several relevant applications for digital libraries such as paper browsing aids, reading recommendations, qualified citation indexing, or fine-grained impact factor calculation. Most citation classifi- cation approaches described so far heavily rely on rule systems and patterns tailored …...

In this paper, we focus on the tasks of cross-domain sentiment classification. We find across different domains, features with some types of part-of-speech (POS) tags are domain-dependent, while some others are domain-free. Based on this finding, we proposed a POS-based ensemble model to efficiently integrate features with different types of …...

In this paper, we introduce the task of identifying general and specific sentences in news articles. Given the novelty of the task, we explore the feasibility of using ex- isting annotations of discourse relations as training data for a general/specific classi- fier. The classifier relies on several classes of features …...

The design of a Spoken Dialogue System (SDS) is a long, iterative and costly pro- cess. Especially, it requires test phases on actual users either for assessment of per- formance or optimization. The number of test phases should be minimized, yet with- out degrading the final performance of the system. …...

As a classic natural language processing technology, topic detection recently attracts more research interests due largely to the rapid development of microblog. The most challenging issue in microblog topic detection is sparse data problem. In this paper, the temporal-author-topic (TAT) model is designed to accomplish microblog topic detection in two …...

Cross-lingual document clustering (CLDC) is the task to automatically or- ganize a large collection of cross-lingual documents into groups considering con- tent or topic. Different from the tradition- al hard matching strategy, this paper ex- tends traditional generalized vector space model (GVSM) to handle cross-lingual cases, referred to as CLGVSM, …...

Named entities and WordNet words are im- portant in defining the content of a text in which they occur. Named entities have onto- logical features, namely, their aliases, classes, and identifiers. WordNet words also have on- tological features, namely, their synonyms, hypernyms, hyponyms, and senses. Those fea- tures of concepts …...

Entity linking maps name mentions in context to entries in a knowledge base through resolv- ing the name variations and ambiguities. In this paper, we propose two advancements for entity linking. First, a Wikipedia-LDA method is proposed to model the contexts as the prob- ability distributions over Wikipedia categories, which …...

Phylogenetic methods are used to build evolutionary trees of languages given character data that may include lexical, phonological, and morphological informa- tion. Such data rarely admits a perfect phylogeny. We explore the use of the more permissive conservative Dollo phy- logeny as an alternative or complementary approach. We propose a …...

We show that transductive (cross-domain) learning is an important consideration in building a general-purpose language iden- tification system, and develop a feature selection method that generalizes across domains. Our results demonstrate that our method provides improvements in transductive transfer learning for language identification. We provide an implementa- tion of the …...

The evaluation of named entity recogni- tion (NER) methods is an active field of research. This includes the recognition of named entities in speech transcripts. Evaluating NER systems on automatic speech recognition (ASR) output whereas human reference annotation was prepared on clean manual transcripts raises difficult alignment issues. These issues …...

This paper addresses a semantic tree-to- string alignment problem: indexing spo- ken documents with known hierarchical semantic structures, with the goal to help index and access such archives. We pro- pose and study a number of alignment models of different modeling capabilities and time complexities to provide a com- prehensive …...