Articles

Most previous work on multilingual sentiment analysis has focused on methods to adapt sentiment resources from resource-rich languages to resource-poor languages. We present a novel approach for joint bilingual sentiment classification at the sentence level that augments available labeled data in each language with unlabeled parallel data. We rely on …...

Consumers increasingly rate, review and re- search products online (Jansen, 2010; Litvin et al., 2008). Consequently, websites con- taining consumer reviews are becoming tar- gets of opinion spam. While recent work has focused primarily on manually identifi- able instances of opinion spam, in this work we study deceptive opinion spam—fictitious …...

This paper proposes the use of local his- tograms (LH) over character n-grams for au- thorship attribution (AA). LHs are enriched histogram representations that preserve se- quential information in documents; they have been successfully used for text categorization and document visualization using word his- tograms. In this work we explore …...

Information retrieval (IR) and figurative language processing (FLP) could scarcely be more different in their treatment of lan- guage and meaning. IR views language as an open-ended set of mostly stable signs with which texts can be indexed and re- trieved, focusing more on a text’s potential relevance than its …...

This paper presents a novel approach for lever- aging automatically extracted textual knowl- edge to improve the performance of control applications such as games. Our ultimate goal is to enrich a stochastic player with high- level guidance expressed in text. Our model jointly learns to identify text that is relevant …...

N-gram language models are a major resource bottleneck in machine translation. In this pa- per, we present several language model imple- mentations that are both highly compact and fast to query. Our fastest implementation is as fast as the widely used SRILM while re- quiring only 25% of the storage. …...

We introduce a novel Bayesian approach for deciphering complex substitution ciphers. Our method uses a decipherment model which combines information from letter n-gram lan- guage models as well as word dictionaries. Bayesian inference is performed on our model using an efficient sampling technique. We evaluate the quality of the Bayesian …...

Topic models have been used extensively as a tool for corpus exploration, and a cottage in- dustry has developed to tweak topic models to better encode human intuitions or to better model data. However, creating such extensions requires expertise in machine learning unavail- able to potential end-users of topic modeling …...

This paper presents an exponential model for translation into highly inflected languages which can be scaled to very large datasets. As in other recent proposals, it predicts target- side phrases and can be conditioned on source- side context. However, crucially for the task of modeling morphological generalizations, it estimates feature …...

We introduce a novel semi-automated metric, MEANT, that assesses translation utility by match- ing semantic role fillers, producing scores that cor- relate with human judgment as well as HTER but at much lower labor cost. As machine transla- tion systems improve in lexical choice and flu- ency, the shortcomings of …...

State-of-the-art statistical machine translation (MT) systems have made significant progress towards producing user-acceptable translation output. However, there is still no efficient way for MT systems to inform users which words are likely translated correctly and how confident it is about the whole sentence. We propose a novel framework to predict …...

This paper presents an attempt at building a large scale distributed composite language model that simultaneously accounts for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content under a directed Markov ran- dom field paradigm. The composite language model has been trained by performing a con- …...

A lack of standard datasets and evaluation metrics has prevented the field of paraphras- ing from making the kind of rapid progress enjoyed by the machine translation commu- nity over the last 15 years. We address both problems by presenting a novel data collection framework that produces highly parallel text …...

We demonstrate how supervised discrimina- tive machine learning techniques can be used to automate the assessment of ‘English as a Second or Other Language’ (ESOL) examina- tion scripts. In particular, we use rank prefer- ence learning to explicitly model the grade re- lationships between scripts. A number of dif- ferent …...

We describe an annotation tool developed to as- sist in the creation of multimodal action- communication corpora from on-line massively multi-player games, or MMGs. MMGs typically involve groups of players (5-30) who control their avatars1, perform various activities (quest- ing, competing, fighting, etc.) and communicate via chat or speech using …...

It has been widely recognized that one of the most difficult and intriguing problems in natural language processing (NLP) is how to cope with idiosyncratic multiword expressions. This paper presents an overview of the comprehensive dictionary (JDMWE) of Japanese multiword expressions. The JDMWE is characterized by a large notational, syntactic, …...

Sentiment analysis on Twitter data has attract- ed much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we clas- sify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral senti- ments about …...

Unsupervised vector-based approaches to se- mantics can model rich lexical meanings, but they largely fail to capture sentiment informa- tion that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsuper- vised and supervised techniques …...

We describe a sentiment classification method that is applicable when we do not have any la- beled data for a target domain but have some labeled data for multiple other domains, des- ignated as the source domains. We automat- ically create a sentiment sensitive thesaurus using both labeled and unlabeled …...

Joint sentiment-topic (JST) model was previ- ously proposed to detect sentiment and topic simultaneously from text. The only super- vision required by JST model learning is domain-independent polarity word priors. In this paper, we modify the JST model by in- corporating word polarity priors through mod- ifying the topic-word Dirichlet …...