Articles

This paper presents our research on automatic question classification for Vietnamese using machine learning approaches. We have experimented with several machine learning algorithms utilizing two kinds of feature groups: bag-of-words and keywords. Our research focuses on two most important tasks which are corpus building and features extraction by crawling data …...

Since the machines become more and more intelligent, it is reasonable to expect the automatic construction of text classi- fiers by given just the objective categories. As trade-off solutions, existing researches usually provide additional information to the category terms to enhance the perfor- mance of a classifier. Unique from them, …...

The wealth of information present in the World Wide Web has made internet search a de-facto medium for obtaining any re- quired information. Users typically spec- ify short and/or ambiguous queries and expect the answer to appear at the top. Hence, it can be extremely important to produce a diverse …...

The popularity of microblogging systems has resulted in a new form of Web data – microtext – which is very different from conventional well-written text. Microtext often has the characteristics of informal- ity, brevity, and varied grammar, which poses new challenges in applying tradi- tional clustering algorithms to analyze mi- …...

We propose a supervised classification ap- proach for automatically determining the polarities of medical sentences. Our po- larity classification approach is context- sensitive, meaning that the same sentence may have differing polarities depending on the context. Using a set of carefully se- lected features, we achieve 84.7% accu- racy, which …...

Paraphrase extraction relying on a single factor such as distribution similarity or translation similarity might lead to the loss of some linguistic properties. In this paper, we propose a paraphrase extraction framework, which accommodates various linguistically motivated factors to optimize the quality of paraphrase extraction. The major contributions of this …...

Mentions of event-argument relations, in par- ticular dependency paths between event- referring words and argument-referring words, can be decomposed into meaningful compo- nents arranged in a regular way, such as those indicating the type of relations and the others allowing relations with distant arguments (e.g., coordinate conjunction). We argue that …...

Supervised training of models for semantic relation extraction has yielded good perfor- mance, but at substantial cost for the annota- tion of large training corpora. Active learn- ing strategies can greatly reduce this annota- tion cost. We present an efficient active learning framework that starts from a better balance between …...

In this study we investigate how we can learn both: (a) syntactic classes that cap- ture the range of predicate argument struc- tures (PASs) of a word and the syntactic alternations it participates in, but ignore large semantic differences in the compo- nent words; and (b) syntactico-semantic classes that capture …...

Can natural language processing be used to predict the likelihood that a musician will commit or has committed suicide? In order to explore this idea, we built a cor- pus of songs that includes a development set, a training set, and a test set, all con- sisting of different lyricists. …...

In this paper we take an important step to- wards completely unsupervised stemming by giving a scheme for semi supervised stemming. The input to the system is a list of word forms and suffixes. The motivation of the work comes from the need to create a root or stem identifier …...

Emotion is an instinctive state of mind aroused by some specific objects or situation. Ex- change of textual information is an important medium for communication and contains a rich set of emotional expressions. The compu- tational approaches to emotion analysis in tex- tual data require annotated lexicons with po- larity …...

Text mining studies have started to investigae rela- tions between positive and negative opinions and pa- tients’ physical health. Several studies linked the per- sonal lexicon with health and the health-related be- havior of the individual. However, few text mining studies were performed to analyze opinions expressed in a large …...

There are many examples in which a word changes its polarity from domain to do- main. For example, unpredictable is pos- itive in the movie domain, but negative in the product domain. Such words cannot be entered in a “universal sentiment lexi- con” which is supposed to be a repository …...

The problem of text summarization for a collection of documents is defined as the problem of selecting a small subset of sentences so that the contents and mean- ing of the original document set are pre- served in the best possible way. In this paper we present a linear model …...

This paper presents a graph-based model that integrates prosodic features into an unsupervised speech summarization framework without any lexical informa- tion. In particular it builds on previous work using mutually reinforced random walks, in which a two-layer graph struc- ture is used to select the most salient ut- terances of …...

In Chinese abbreviation prediction, prior studies are limited on positive full forms. This lab assumption is problematic in real- world applications, which have a large portion of negative full forms (NFFs). We propose solutions to solve this problem of generalized abbreviation prediction. Ex- periments show that the proposed unified method …...

This paper presents a methodology to identify polysemous German prepositions by exploring their vector spatial proper- ties. We apply two cluster evaluation metrics (the Silhouette Value (Kaufman and Rousseeuw, 1990) and a fuzzy ver- sion of the V-Measure (Rosenberg and Hirschberg, 2007)) as well as various cor- relations, to exploit …...

Feature augmentation is a well-known method for domain adaptation and has been shown to be effective when tested on several NLP tasks (Daume III, 2007). However, a limitation of the method is that it requires labeled data from the target do- main and very often such data is unavail- able. …...

This paper presents a new machine-learning Chinese word segmentation (CWS) approach, which defines CWS as a break-point classifi- cation problem; the break point is the bound- ary of two subsequent words. Further, this paper exploits a support vector machine (SVM) classifier, which learns the segmenta- tion rules of the Chinese …...