N-Grams

In recent years there is much interest in word cooccurrence relations, such as n-grams, verb- object combinations, or cooccurrence within a limited context. This paper discusses how to estimate the …...

Statistical language models are useful because they can provide probabilis- tic information upon uncertain decision making. The most common statistic is n-grams measuring word cooccurrences in texts. The method suffers …...

It is shown that for a large corpus, Zipf 's law for both words in English and characters in Chinese does not hold for all ranks. The frequency falls below …...

In this paper, experiments on automatic extraction of keywords from abstracts us- ing a supervised machine learning algo- rithm are discussed. The main point of this paper is that by …...

In this paper, we explore the use of Random Forests (RFs) (Amit and Geman, 1997; Breiman, 2001) in language modeling, the problem of predicting the next word based on words …...

. In this paper, we present a language model based on clusters obtained by applying regular expressions to the training data and, thus, discriminating several different sentence types as, e.g. …...

An unsupervised learning method, based on corpus linguistics and special lan- guage terminology, is described that can extract time-varying information from text streams. The method is shown to be ‘language-independent’ …...

We propose a machine learning based method of sentiment classification of sen- tences using word-level polarity. The polari- ties of words in a sentence are not always the same as …...

We propose an automatic machine translation (MT) evaluation metric that calculates a sim- ilarity score (based on precision and recall) of a pair of sentences. Unlike most metrics, we compute …...

. In this paper we present a simple approach to discover gender and animacy knowledge for person mention detection. We learn noun-gender and noun-animacy pair counts from web-scale n-grams using …...

This paper explores the influence of text pre- processing techniques on plagiarism detection. We examine stop-word removal, lemmatization, number replacement, synonymy recognition, and word generalization. We also look into the …...

AbstractExisting approaches to classifying documents by sentiment include machine learning with features created from n-grams and part of speech. This paper explores a different approach and examines performance of one …...

We present an approach to perform exter- nal plagiarism analysis by applying sev- eral similarity detection techniques, such as lexical measures and a textual entail- ment recognition system developed by …...

This study aims to assess the usefulness of multi-word expressions (MWEs) as fea- tures for a readability formula that pre- dicts the difficulty of texts for French as a foreign …...

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 …...

We introduce a method for learning to predict text completion given a source text and partial translation. In our approach, predictions are offered aimed at alleviating users’ burden on lexical …...

The Discriminative Word Lexicon (DWL) is a maximum-entropy model that pre- dicts the target word probability given the source sentence words. We present two ways to extend a DWL to …...

Sentence compression techniques often assemble output sentences using frag- ments of lexical sequences such as n- grams or units of syntactic structure such as edges from a dependency tree repre- …...

We propose and assess the novel idea of using automatically induced constructions as a unit of analysis for corpus-based discourse analysis. Automated techniques are needed in order to elucidate important …...

In this paper, we systematically explore lexicalized and non-lexicalized local syntactic features for the task of Native Language Identification (NLI). We investigate different types of feature representations in single- and …...

In this paper we describe the system sub- mitted for the SemEval 2014 Task 9 (Sen- timent Analysis in Twitter) Subtask B. Our contribution consists of a supervised ap- proach …...

Post-editing the output of a statistical machine translation (SMT) system to obtain high-quality translation has become an increasingly common application of SMT, which henceforth we re- fer to as post-editing-based …...

After the Great East Japan Earthquake in 2011, an abundance of false rumors were dis- seminated on Twitter that actually hindered rescue activities. This work presents a method for recognizing …...

Document sentiment classification is of- ten processed by applying machine learn- ing techniques, in particular supervised learning which consists basically of two major steps: feature extraction and train- ing the …...

AbstractWe study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In …...

This paper describe a weakly supervised so- lution for detecting stance in tweets, submit- ted to the SemEval 2016 Stance Task. Our approach is based on the premise that stance …...

AbstractThis paper describes a system developed for a shared sentiment analysis task and its subtasks organized by SemEval-2017. A key feature of our system is the embedded ability to detect …...

AbstractThis paper describes a supervised solution for detecting the polarity scores of tweets or headline news in the financial domain, submitted to the SemEval 2017 Fine-Grained Sentiment Analysis on Financial …...

AbstractThis paper describes the model UdL we proposed to solve the semantic textual similarity task of SemEval 2017 workshop. The track we participated in was estimating the semantics relatedness of …...

We release a Python module containing several tools to build analogical grids from words contained in a corpus. The module implements several previously presented algorithms. The tools are language-independent. This …...

AbstractThis paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a …...

AbstractWe propose a new type of representation learning method that models words, phrases and sentences seamlessly. Our method does not depend on word segmentation and any human-annotated resources (e.g., word …...

AbstractChinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks for Chinese language processing. Previous studies have demonstrated that jointly performing them can be an effective one-step solution …...

AbstractSupertagging is conventionally regarded as an important task for combinatory categorial grammar (CCG) parsing, where effective modeling of contextual information is highly important to this task. However, existing studies have …...

AbstractHyperpartisan news show an extreme manipulation of reality based on an underlying and extreme ideological orientation. Because of its harmful effects at reinforcing one’s bias and the posterior behavior of …...

AbstractMost recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the …...

AbstractLarge pre-trained models such as BERT are known to improve different downstream NLP tasks, even when such a model is trained on a generic domain. Moreover, recent studies have shown …...