Articles

Aspect-level sentiment classification is a fine- grained task in sentiment analysis. Since it provides more complete and in-depth results, aspect-level sentiment analysis has received much attention these years. In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also …...

A word’s sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to in- duce accurate domain-specific sentiment lex- icons using small sets of …...

We study the problem of identifying individu- als based on their characteristic gaze patterns during reading of arbitrary text. The motiva- tion for this problem is an unobtrusive biomet- ric setting in which a user is observed during access to a document, but no specific chal- lenge protocol requiring the …...

In multilingual question answering, either the question needs to be translated into the docu- ment language, or vice versa. In addition to direction, there are multiple methods to per- form the translation, four of which we explore in this paper: word-based, 10-best, context- based, and grammar-based. We build a fea- …...

We present a semi-automated framework for constructing factoid question answering (QA) datasets, where an array of question character- istics are formalized, including structure com- plexity, function, commonness, answer cardi- nality, and paraphrasing. Instead of collecting questions and manually characterizing them, we employ a reverse procedure, first generat- ing a kind …...

The advent of social media and its prosperity enable users to share their opinions and views. Understanding users’ emotional states might provide the potential to create new business opportunities. Automatically identifying user- s’ emotional states from their texts and clas- sifying emotions into finite categories such as joy, anger, disgust, …...

In this paper we address the question of how to render sequence-level networks better at handling structured input. We propose a ma- chine reading simulator which processes text incrementally from left to right and performs shallow reasoning with memory and atten- tion. The reader extends the Long Short-Term Memory architecture …...

Verbal comprehension questions appear very frequently in Intelligence Quotient (IQ) tests, which measure human’s verbal ability includ- ing the understanding of the words with mul- tiple senses, the synonyms and antonyms, and the analogies among words. In this work, we explore whether such tests can be solved automatically by the …...

Opinionated expression extraction is a cen- tral problem in fine-grained sentiment anal- ysis. Most existing works focus on either generic subjective expression or aspect ex- pression extraction. However, in opinion mining, it is often desirable to mine the as- pect specific opinion expressions (or aspect- sentiment phrases) containing both the …...

Among the alignment models used in statis- tical machine translation (SMT), the hidden Markov model (HMM) is arguably the most elegant: it performs consistently better than IBM Model 3 and is very close in perfor- mance to the much more complex IBM Model 4. In this paper we discuss a …...

Models of neural machine translation are of- ten from a discriminative family of encoder- decoders that learn a conditional distribution of a target sentence given a source sentence. In this paper, we propose a variational model to learn this conditional distribution for neu- ral machine translation: a variational encoder- decoder …...

Structural isomorphism between languages benefits the performance of cross-lingual ap- plications. We propose an automatic al- gorithm for cross-lingual similarization of dependency grammars, which automatically learns grammars with high cross-lingual sim- ilarity. The algorithm similarizes the an- notation styles of the dependency grammars for two languages in the level of …...

Recent work on machine translation has used crowdsourcing to reduce costs of manual eval- uations. However, crowdsourced judgments are often biased and inaccurate. In this pa- per, we present a statistical model that ag- gregates many manual pairwise comparisons to robustly measure a machine translation system’s performance. Our method applies …...

Word embeddings allow natural language pro- cessing systems to share statistical information across related words. These embeddings are typically based on distributional statistics, mak- ing it difficult for them to generalize to rare or unseen words. We propose to improve word embeddings by incorporating morphological information, capturing shared sub-word fea- …...

Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many stud- ies have shown the effectiveness of …...

We present the Structured Weighted Violations Perceptron (SWVP) algorithm, a new struc- tured prediction algorithm that generalizes the Collins Structured Perceptron (CSP, (Collins, 2002)). Unlike CSP, the update rule of SWVP explicitly exploits the internal structure of the predicted labels. We prove the convergence of SWVP for linearly separable training …...

Multi-modal distributional models learn grounded representations for improved performance in semantics. Deep visual representations, learned using convolutional neural networks, have been shown to achieve particularly high performance. In this study, we systematically compare deep visual representation learning techniques, exper- imenting with three well-known network architectures. In addition, we explore the …...

In this paper we introduce Latent Tree Lan- guage Model (LTLM), a novel approach to language modeling that encodes syntax and semantics of a given sentence as a tree of word roles. The learning phase iteratively updates the trees by moving nodes according to Gibbs sampling. We introduce two algorithms …...

Given a set of documents from a specific do- main (e.g., medical research journals), how do we automatically build a Knowledge Graph (KG) for that domain? Automatic identifica- tion of relations and their schemas, i.e., type signature of arguments of relations (e.g., un- dergo(Patient, Surgery)), is an important first step …...

Lexical taxonomies are graph-like hierarchi- cal structures that provide a formal representa- tion of knowledge. Most knowledge graphs to date rely on is-a (hypernymic) relations as the backbone of their semantic structure. In this paper, we propose a supervised distributional framework for hypernym discovery which op- erates at the sense …...