Articles

This article aims to analyze how agreement regarding the central unit (macrostructure) influ- ences agreement when establishing rhetorical relations (microstructure). To do so, the authors conducted an empirical study of abstracts from research articles in three domains (medicine, ter- minology, and science) in the framework of Rhetorical Structure Theory (RST). …...

Recently there has been growing interest in the application of approaches from the text classi- fication literature to fine-grained problems of textual stylometry. This paper seeks to answer a question which has concerned the translation studies community: how does a literary transla- tor’s style vary across their translations of different …...

Automatically inferring new relations from already existing ones is a way to improve the qual- ity and coverage of a lexical network and to perform error detection. In this paper, we devise such an approach for the crowdsourced JeuxDeMots lexical network and we focus especially on word refinements. We first …...

Word Sense Induction (WSI) aims to automatically induce meanings of a polysemous word from unlabeled corpora. In this paper, we first propose a novel Bayesian parametric model to WSI. Unlike previous work, our research introduces a layer of hidden concepts and view senses as mixtures of concepts. We believe that …...

In this paper, we have studied the effect of two important factors influencing text readability in Bangla: the target reader and text properties. Accordingly, at first we have built a novel Bangla readability dataset of 135 documents annotated by 50 readers from two different backgrounds. We have identified 20 different …...

In this paper we present a readability assessment system for Basque, ErreXail, which is going to be the preprocessing module of a Text Simplification system. To that end we compile two corpora, one of simple texts and another one of complex texts. To analyse those texts, we imple- ment global, …...

In this paper, a large scale modern Tibetan text corpus is built, which includes about 190 thou- sands documents, 67.21 million words, 93.66 million syllables in total. Based on the corpus, statistics are made in several language units in different granularities. Statistical data show that : a syllable has 3.26 …...

Results from psychology show a connection between a speaker’s expertise in a task and the lan- guage he uses to talk about it. In this paper, we present an empirical study on using linguistic evidence to predict the expertise of a speaker in a task: playing chess. Instructional chess litera- …...

NLP methods for automatic information access to rich technological knowledge sources like patents are of great value. One important resource for accessing this knowledge is the tech- nical terminology of the patent domain. In this paper, we address the problem of automatic terminology acquisition (ATA), i.e., the problem of automatically …...

Word Ordering Errors (WOEs) are the most frequent type of grammatical errors at sentence level for non-native Chinese language learners. Learners taking Chinese as a foreign language often place character(s) in the wrong places in sentences, and that results in wrong word(s) or ungrammatical sentences. Besides, there are no clear …...

We present a novel approach for rapidly developing a corpus with discourse annotations using crowdsourcing. Although discourse annotations typically require much time and cost owing to their complex nature, we realize discourse annotations in an extremely short time while retaining good quality of the annotations by crowdsourcing two annotation subtasks. …...

In this paper, we present a machine learning approach for word sense alignment (WSA) which combines distances between senses in the graph representations of lexical-semantic resources with gloss similarities. In this way, we significantly outperform the state of the art on each of the four datasets we consider. Moreover, we …...

With a large amount of complex network data available from multiple data sources, how to effec- tively combine these available data with existing auxiliary information such as item content into the same recommendation framework for more accurately modeling user preference is an inter- esting and significant research topic for various …...

This paper presents a non-projective dependency parsing system that is transition-based and op- erates in three steps. The three steps include one classical method for projective dependency parsing and two inverse methods predicting separately the right and left non-projective depen- dencies. Splitting the parsing allows to increase the scores on …...

This paper presents an unsupervised approach for the task of clustering the results of a search engine when the query is a person name shared by different individuals. We propose an algo- rithm that calculates the number of clusters and establishes the groups of web pages according to the different …...

This paper presents a machine learning approach to sarcasm detection on Twitter in two lan- guages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in …...

We present a multi-view annotation framework for Chinese treebanking, which uses dependen- cy structures as the base view and supports conversion into phrase structures with minimal loss of information. A multi-view Chinese treebank was built under the proposed framework, and the first release (PMT 1.0) containing 14,463 sentences is be …...

The task of recommending hashtags for microblogs has been received considerable attention in recent years, and many applications can reap enormous benefits from it. Various approaches have been proposed to study the problem from different aspects. However, the impacts of temporal and personal factors have rarely been considered in the …...

This paper presents an empirical study on using syntactic and semantic information for Concept Segmentation and Labeling (CSL), a well-known component in spoken language understand- ing. Our approach is based on reranking N-best outputs from a state-of-the-art CSL parser. We perform extensive experimentation by comparing different tree-based kernels with a …...

In this paper, we propose to build large-scale sentiment lexicon from Twitter with a representation learning approach. We cast sentiment lexicon learning as a phrase-level sentiment classification task. The challenges are developing effective feature representation of phrases and obtaining training data with minor manual annotations for building the sentiment classifier. …...