Articles

Recognizing Textual Entailment (RTE) is to predict whether one text fragment can semantically infer another, which is re- quired across multiple applications of nat- ural language processing. The conven- tional alignment scheme, which is devel- oped for machine translation, only marks the paraphrases and hyponyms to justify the entailment pairs, …...

The context type and similarity calcula- tion are two essential features of a distri- butional similarity scheme (DSS). In this paper, we propose a hierarchical semantic- aware DSS that exploits semantic rela- tion words as extra context information to guide the similarity calculation. First, we define and extract five types …...

Event factuality is information about whether events mentioned in natural lan- guage correspond to either actual events that have occurred in the real world or events that are of uncertain interpreta- tion. Factuality analysis is useful for infor- mation extraction and textual entailment recognition, among others, but sufficient performance has …...

We propose a novel method to detect cy- berbullying entries on the Internet. “Cy- berbullying” is defined as humiliating and slandering behavior towards other people through Internet services, such as BBS, Twitter or e-mails. In Japan members of Parent-Teacher Association (PTA) per- form manual Web site monitoring called “net-patrol” to …...

Sentiment analysis of reviews traditionally ig- nored the association between the features of the given product domain. The hierarchical re- lationship between the features of a product and their associated sentiment that influence the polarity of a review is not dealt with very well. In this work, we analyze the …...

Sentiment classification is able to help people automatically analyze customers’ opinions from the large corpus. In this paper, we collect some Chinese movie reviews from Bulletin Board System and aim at making sentiment classification so as to extract several frequent opinion words in some movie elements such as plots, actors/actresses, …...

Queries asked on search engines nowa- days increasingly fall in full natural lan- guage, which refer to Natural Language queries (NL queries). Parsing that kind of queries for the purpose of understanding user’s query intent is an essential factor to search engine. To this end, a hierarchical structure is introduced …...

Keyphrase extraction is the task of iden- tifying single or multi-word expressions that represent the main topics of a doc- ument. In this paper we present Topi- cRank, a graph-based keyphrase extrac- tion method that relies on a topical rep- resentation of the document. Candidate keyphrases are clustered into topics …...

From an efficiency viewpoint, information extraction means to filter the relevant por- tions of natural language texts as fast as possible. Given an extraction task, differ- ent pipelines of algorithms can be devised that provide the same precision and recall but that vary in their run-time due to dif- ferent …...

Discriminative models such as logistic re- gression profit from the ability to incorpo- rate arbitrary rich features; however, com- plex dependencies among overlapping fea- tures can often result in weight undertrain- ing. One popular method that attempts to mitigate this problem is logarithmic opin- ion pools (LOP), which is a …...

Work on information retrieval has shown that language model smoothing leads to more accurate estimation of document models and hence is crucial for achiev- ing good retrieval performance. Several smoothing methods have been proposed in the literature, using either semantic or po- sitional information. In this paper, we pro- pose …...

We design a language model based on a generative dependency structure for sen- tences. The parameter of the model is the probability of a dependency N-gram, which is composed of lexical words with four kinds of extra tags used to model the dependency relation and valence. We fur- ther propose …...

We present three approaches to word sense disambiguation that use Wikipedia as a source of sense annotations. Starting from a basic monolingual approach, we de- velop two multilingual systems: one that uses a machine translation system to cre- ate multilingual features, and one where multilingual features are extracted primar- ily …...

For many NLP applications such as In- formation Extraction and Sentiment De- tection, it is of vital importance to distin- guish between synonyms and antonyms. While the general assumption is that dis- tributional models are not suitable for this task, we demonstrate that using suitable features, differences in the contexts …...

 Concept extraction is a primary subtask of on- tology construction. It is difficult to extract new concepts from traditional text corpus. Moreover, building a single ontology for mul- tiple-topic corpus may lead to misconception. To deal with these problems, this paper pro- poses a novel framework to extract topical …...

The inherent morphological complexity of languages such as Arabic entails the ex- ploration of language traits that could be valuable to the task of detecting and clas- sifying sentiment within text. This pa- per investigates the relevance of using the roots of words as input features into a sentiment analysis …...

This paper presents a method of improving lexicon-based review classification by merg- ing multiple sentiment dictionaries, and selec- tively removing and switching the contents of merged dictionaries. First, we compare the positive/negative book review classification performance of eight individual sentiment dic- tionaries. Then, we select the seven dictionar- ies with …...

The imbalanced sentiment distribution of microblogs induces bad performance of binary classifiers on the minority class. To address this problem, we present a semi- supervised method for sentiment classi- fication of Chinese microblogs. This method is similar to self-training, except that, a set of labeled samples is reserved for a …...

Language model is an essential part in sta- tistical machine translation, but traditional n-gram language models can only capture a limited local context in the translated sentence, thus lacking the global informa- tion for prediction. This paper describes a novel topic-triggered language model, which takes into account the topical con- …...

Phrase-based machine translation like other data driven approaches, are often plagued by irregularities in the transla- tions of words in morphologically rich lan- guages. The phrase-pairs and the lan- guage models are unable to capture the long range dependencies which decide the inflection. This paper makes the first at- tempt …...