Articles

Word Sense Induction (WSI) is the task of au- tomatically inducing the different senses of a target word from unannotated text. Traditional approaches based on the vector space model (VSM) represent each context of a target word as a vector of selected features (e.g. the words occurring in the context). …...

We propose a new method for word sense disambiguation for verbs. In our method, sense-dependent selectional preference of verbs is obtained through the probabilistic model on the lexical network. The mean- field approximation is employed to com- pute the state of the lexical network. The outcome of the computation is …...

This paper proposes a weakly-supervised approach for extracting instances of se- mantic classes. This method constructs simple wrappers automatically based on specified seed instances and uses a com- pression model to assess the contextual ev- idence of its extraction. By adopting this compression model, our approach can bet- ter avoid …...

This paper addresses the issue of cluster labeling and presents a method for assign- ing labels by using concepts in a machine- readable dictionary. We assume that salient terms in the cluster content have the same hypernym because hypernymic se- mantic relation represents a generalization that goes from specific to …...

Near-synonym sets represent groups of words with similar meaning, which are useful knowl- edge resources for many natural language ap- plications such as query expansion for infor- mation retrieval (IR) and computer-assisted language learning. However, near-synonyms are not necessarily interchangeable in contexts due to their specific usage and syntactic con- …...

Chinese and Spanish are the most spoken lan- guages in the world. However, there is not much research done in machine translation for this language pair. We experiment with the parallel Chinese-Spanish corpus (United Na- tions) to explore alternatives of SMT strate- gies which consist on using a pivot language. …...

Minimum Bayes Risk (MBR) has been used as a decision rule for both single- system decoding and system combination in machine translation. For system com- bination, we argue that common MBR implementations are actually not correct, since probabilities in the hypothesis space cannot be reliably estimated. These imple- mentations achieve …...

This paper presents a novel method to re- move asymmetry between the source and the target languages thereby improving alignment and machine translation (MT) quality. Some words in the source lan- guage are redundant for MT tasks but nec- essary for the source sentence to be gram- matical. This paper …...

We present and evaluate a set of archi- tectures for conversational dialogue sys- tems, exploring rule-based and statistical classification approaches. In a case study, we show that while a rule-based dialogue policy is capable of high performance if perfect natural language understanding is assumed, a direct classification approach that combines …...

Transliteration is the process of proper name translation based on pronunciation. It is an important process in many multilin- gual natural language tasks. A common and essential component of transliteration approaches is a verification mechanism that tests if the two names in different lan- guages are translations of each other. …...

During the last years there is increas- ing interest in methods that perform some kind of weighting of heterogeneous paral- lel training data when building a statistical machine translation system. It was for in- stance observed that training data that is close to the period of the test data is …...

Conditional probabilistic models for word alignment are popular due to the elegant way of handling them in the training stage. However, they have weaknesses such as garbage collection and scale poorly be- yond single word based models (DeNero et al., 2006): not all parameters should ac- tually be used. To …...

The language specific Multiword expressions (MWEs) play important roles in many natural language processing (NLP) tasks. Integrating reduplicated multiword expressions (RMWEs) into the Phrase Based Statistical Machine Translation (PBSMT) to improve translation quality is reported in the present work between Manipuri, a highly agglutinative Tibeto-Burman language and English. In addition, …...

We demonstrate that statistical machine translation (SMT) can be improved sub- stantially by imposing clause-based re- ordering constraints during decoding. Our analysis of clause-wise translation of dif- ferent types of clauses shows that it is ben- eficial to apply these constraints for finite clauses, but not for non-finite clauses. In …...

Word alignment is a fundamental step in machine translation. Current statistical machine translation systems suffer from a major drawback: they only extract rules from 1-best alignments, which adversely affects the rule sets quality due to align- ment mistakes. To alleviate this prob- lem, we extract hierarchical rules from weighted alignment …...

In this paper we study the word-reordering problem in the decoding part of statisti- cal machine translation, but independently from the target language generating pro- cess. In this model, a permuted sentence is given and the goal is to recover the correct order. We introduce a greedy algorithm called Local-(k, …...

This paper investigates clausal data-driven de- pendency parsing. We first motivate a clause as the minimal parsing unit by correlating in- ter- and intra-clausal relations with relation type, depth, arc length and non-projectivity. This insight leads to a two-stage formulation of parsing where intra-clausal relations are identified in the 1st …...

Named Entity Recognition (NER) is a well-known Natural Language Processing (NLP) task, used as a preliminary process- ing to provide a semantic level to more complex tasks. In this paper we describe a new set of named entities having a multi- level tree structure, where base entities are combined to …...

In this paper, we propose a factored pars- ing model consisting of a lexical and a constituent model. The discriminative lex- ical model allows the parser to utilize rich contextual features beyond those encoded in the context-free grammar (CFG) in use. Experiment results reveal that our parser achieves statistically significant …...

Existing work shows that lexical dependen- cies are helpful for constituent tree parsing. However, only first-order lexical dependen- cies have been employed and investigated in previous work. In this paper, we propose a method to employing higher-order1 lexical dependencies for constituent tree evaluation. Our method is based on a parse …...