Articles

The paper addresses the task of automatic in- terpretation of semantic relation in noun com- pounds. The problem has been attempted with both Ontology-based and Statistical ap- proaches, but both approaches having their own limitations. We present a novel VSM- based statistical model which represents each relation with a weighted …...

In this paper we study the identification and verification of simple claims about statistical properties, e.g. claims about the population or the inflation rate of a coun- try. We show that this problem is similar to extracting numerical information from text and following recent work, instead of annotating data for …...

Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We for- mulate the problem of classifying tweet level judgements of rumours as a super- vised learning task. Both supervised and unsupervised domain adaptation are con- sidered, in which …...

First Story Detection is hard because the most accurate systems become pro- gressively slower with each document processed. We present a novel ap- proach to FSD, which operates in constant time/space and scales to very high volume streams. We show that when computing novelty over a large dataset of tweets, …...

Domestic abuse affects people of every race, class, age, and nation. There is sig- nificant research on the prevalence and ef- fects of domestic abuse; however, such re- search typically involves population-based surveys that have high financial costs. This work provides a qualitative analysis of do- mestic abuse using data …...

We consider the task of automatically identifying participants’ motivations in the public health campaign Movember and in- vestigate the impact of the different moti- vations on the amount of campaign dona- tions raised. Our classification scheme is based on the Social Identity Model of Col- lective Action (van Zomeren et …...

We propose a method to detect hidden data in English text. We target a system pre- viously thought secure, which hides mes- sages in tweets. The method brings ideas from image steganalysis into the linguis- tic domain, including the training of a feature-rich model for detection. To iden- tify Twitter …...

We propose a novel data augmentation ap- proach to enhance computational behav- ioral analysis using social media text. In particular, we collect a Twitter corpus of the descriptions of annoying behaviors us- ing the #petpeeve hashtags. In the qual- itative analysis, we study the language use in these tweets, with …...

For sentiment classification, it is often recognized that embedding based on dis- tributional hypothesis is weak in captur- ing sentiment contrast–contrasting words may have similar local context. Based on broader context, we propose to incorporate Theta Pure Dependence (TPD) into the Paragraph Vector method to reinforce top- ical and sentimental …...

We present a novel way of extracting fea- tures from short texts, based on the acti- vation values of an inner layer of a deep convolutional neural network. We use the extracted features in multimodal senti- ment analysis of short video clips repre- senting one sentence each. We use the …...

We present the Trip-MAML dataset, a Multi-Lingual dataset of hotel reviews that have been manually annotated at the sentence-level with Multi-Aspect senti- ment labels. This dataset has been built as an extension of an existent English-only dataset, adding documents written in Ital- ian and Spanish. We detail the dataset construction …...

Sentiment analysis models often use rat- ings as labels, assuming that these rat- ings reflect the sentiment of the accom- panying text. We investigate (i) whether human readers can infer ratings from re- view text, (ii) how human performance compares to a regression model, and (iii) whether model performance is …...

For fine-grained sentiment analysis, we need to go beyond zero-one polarity and find a way to compare adjectives that share a common semantic property. In this paper, we present a semi-supervised ap- proach to assign intensity levels to adjec- tives, viz. high, medium and low, where adjectives are compared when …...

This paper introduces ASTD, an Arabic social sentiment analysis dataset gathered from Twitter. It consists of about 10,000 tweets which are classified as objective, subjective positive, subjective negative, and subjective mixed. We present the properties and the statistics of the dataset, and run experiments using standard par- titioning of the …...

This paper presents a new method to iden- tify sentiment of an aspect of an entity. It is an extension of RNN (Recursive Neu- ral Network) that takes both dependency and constituent trees of a sentence into ac- count. Results of an experiment show that our method significantly outperforms pre- …...

Cross-domain sentiment classification (CSC) aims at learning a sentiment classifier for unlabeled data in the target domain based on the labeled data from a different source domain. Due to the differences of data distribution of two domains in terms of the raw features, the CSC problem is difficult and challenging. …...

Sentiment analysis has been a major area of interest, for which the existence of high- quality resources is crucial. In Arabic, there is a reasonable number of sentiment lexicons but with major deficiencies. The paper presents a large-scale Standard Ara- bic Sentiment Lexicon (SLSA) that is pub- licly available for …...

Modeling the entailment relation over sen- tences is one of the generic problems of natural language understanding. In or- der to account for this problem, we de- sign a theorem prover for Natural Logic, a logic whose terms resemble natural lan- guage expressions. The prover is based on an analytic …...

We present a new approach for unsuper- vised semantic role labeling that lever- ages distributed representations. We in- duce embeddings to represent a predi- cate, its arguments and their complex in- terdependence. Argument embeddings are learned from surrounding contexts involv- ing the predicate and neighboring argu- ments, while predicate embeddings …...

This paper provides the first fully au- tomatic approach for classifying clauses with respect to their aspectual properties as habitual, episodic or static. We bring together two strands of previous work, which address only the related tasks of the episodic-habitual and stative-dynamic distinctions, respectively. Our method combines different sources of …...