Feature Selection

\Ve JH't'::wnt statistical rnodels of Japanese dependency analysis aud report results of some experiments to in- vestigate the pnfonuancc of the models for the use fo a partical parsing system. …...

Named Entity (NE) recognition is a task in which proper nouns and numerical information are ex- tracted from documents and are classified into cat- egories such as person, organization, and …...

This paper examines feature selection for log linear models over rich constraint-based grammar (HPSG) representations by building decision trees over fea- tures in corresponding probabilistic context free grammars (PCFGs). We …...

We describe experiments with a Naive Bayes text classifier in the context of anti- spam E-mail filtering, using two different statistical event models: a mul- ti-variate Bernoulli model and a …...

We cluster verbs into lexical semantic classes, using a general set of noisy features that cap- ture syntactic and semantic properties of the verbs. The feature set was previously shown …...

The present work advances the accu- racy and training speed of discrimina- tive parsing. Our discriminative parsing method has no generative component, yet surpasses a generative baseline on con- stituent …...

Lexical features are key to many ap- proaches to sentiment analysis and opin- ion detection. A variety of representations have been used, including single words, multi-word Ngrams, phrases, and lexico- …...

Statistical machine learning methods are em- ployed to train a Named Entity Recognizer from annotated data. Methods like Maxi- mum Entropy and Conditional Random Fields make use of features for …...

Financial investors trade on the basis of infor- mation, and in particular, on the likelihood that a piece of information will impact the mar- ket. The ability to predict this …...

In this paper, we propose a novel way to include unsupervised feature selection meth- ods in probabilistic taxonomy learning mod- els. We leverage on the computation of logistic regression to …...

Pathology reports are used to store infor- mation about cells and tissues of a patient, and they are crucial to monitor the health of individuals and population groups. In this …...

In this paper, we provide a theoretical framework for feature selection in tree ker- nel spaces based on gradient-vector com- ponents of kernel-based machines. We show that a huge number …...

A medical publication may or may not present an outcome. When an outcome is present, its polarity may be positive, negative or neu- tral. Information about the polarity of an …...

In this paper, we propose a differential evolution (DE) based two-stage evolutionary approach for named entity recognition (NER). The first stage concerns with the problem of relevant feature selection for …...

AbstractCollocations can be defined as words that occur together significantly more often than it would be expected by chance. Many natural language processing applications such as natural language generation, word …...

Geolocation prediction is vital to geospatial applications like localised search and local event detection. Predominately, social media geolocation models are based on full text data, including common words with no …...

. This paper presents a set of experiments aiming to (1) assess the contribution of NLP to the specific issue of the readability of texts for French as a foreign …...

This paper describes a set of experi- ments on two sub-tasks of Quality Esti- mation of Machine Translation (MT) out- put. Sentence-level ranking of alternative MT outputs is done with …...

We describe the results of our submissions to the WMT13 Shared Task on Quality Estimation (subtasks 1.1 and 1.3). Our submissions use the framework of Gaus- sian Processes to investigate …...

We present our system WSD2 which partic- ipated in the Cross-Lingual Word-Sense Dis- ambiguation task for SemEval 2013 (Lefever and Hoste, 2013). The system closely resem- bles our winning system …...

AbstractAutomated emotion recognition has a number of applications in Interactive Voice Response systems, call centers, etc. While employing existing feature sets and methods for automated emotion recognition has already achieved …...

Argument mining studies in natural language text often use lexical (e.g. n-grams) and syn- tactic (e.g. grammatical production rules) fea- tures with all possible values. In prior work on a …...

We describe our system for WMT2015 Shared Task on Quality Estimation, task 1, sentence-level prediction of post-edition effort. We use baseline features, Latent Semantic Indexing based features and fea- tures …...

AbstractThe paper describes a comparative study of existing and novel text preprocessing and classification techniques for domain detection of user utterances. Two corpora are considered. The first one contains customer …...

Digital personal assistants are becoming both more common and more useful. The major NLP challenge for personal assis- tants is machine understanding: translat- ing natural language user commands into an …...

This paper gives the description of the TGB system submitted to the Aspect Based Senti- ment Analysis Task of SemEval-2016 (Task 5). The system is built on linear binary classifiers …...

AbstractIn this paper, we present a study for the identification of authors’ national variety of English in texts from social media. In data from Facebook and Twitter, information about the …...

AbstractThe computational complexity of linear-chain Conditional Random Fields (CRFs) makes it difficult to deal with very large label sets and long range dependencies. Such situations are not rare and arise …...

This work presents a comparative study between two different approaches to build an automatic classification system for Modality values in the Portuguese language. One approach uses a single multi-class classifier …...

AbstractJudgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction. While existing work merely predicts the total …...

AbstractSelective rationalization has become a common mechanism to ensure that predictive models reveal how they use any available features. The selection may be soft or hard, and identifies a subset …...

AbstractThis report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. Our team dealt …...

AbstractDeep learning models have achieved great success on the task of Natural Language Inference (NLI), though only a few attempts try to explain their behaviors. Existing explanation methods usually pick …...