Articles

This paper offers an approach for governments to harness the information contained in social media in order to make public inspections and disclosure more efficient. As a case study, we turn to restaurant hygiene inspections – which are done for restaurants throughout the United States and in most of the …...

We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task. The extended model incorporates the idea the words closer to the target word are more relevant in predicting its sense. The pro- posed model is very simple yet effective when evaluated …...

This research describes efforts to use crowd- sourcing to improve the validity of the seman- tic predicates in VerbNet, a lexicon of about 6300 English verbs. The current semantic predicates can be thought of semantic prim- itives, into which the concepts denoted by a verb can be decomposed. For example, …...

We present a novel unsupervised approach to detecting the compositionality of multi-word expressions. We compute the compositional- ity of a phrase through substituting the con- stituent words with their “neighbours” in a se- mantic vector space and averaging over the distance between the original phrase and the substituted neighbour phrases. …...

In this paper, we propose a walk-based graph kernel that generalizes the notion of tree- kernels to continuous spaces. Our proposed approach subsumes a general framework for word-similarity, and in particular, provides a flexible way to incorporate distributed repre- sentations. Using vector representations, such an approach captures both distributional se- …...

Sentence completion is a challenging seman- tic modeling task in which models must choose the most appropriate word from a given set to complete a sentence. Although a variety of language models have been ap- plied to this task in previous work, none of the existing approaches incorporate syntactic in- …...

In this paper we present a novel approach to automatic creation of anchor texts for hyper- links in a document pointing to similar doc- uments. Methods used in this approach rank parts of a document based on the similarity to a presumably related document. Ranks are then used to automatically …...

We introduce bilingual word embeddings: se- mantic embeddings associated across two lan- guages in the context of neural language mod- els. We propose a method to learn bilingual embeddings from a large unlabeled corpus, while utilizing MT word alignments to con- strain translational equivalence. The new em- beddings significantly out-perform …...

We explore the application of neural language models to machine translation. We develop a new model that combines the neural proba- bilistic language model of Bengio et al., rec- tified linear units, and noise-contrastive esti- mation, and we incorporate it into a machine translation system both by reranking k-best lists …...

This paper presents a novel word reordering model that employs a shift-reduce parser for inversion transduction grammars. Our model uses rich syntax parsing features for word re- ordering and runs in linear time. We apply it to postordering of phrase-based machine trans- lation (PBMT) for Japanese-to-English patent tasks. Our experimental …...

Online resources, such as Wiktionary, provide an accurate but incomplete source of idiomatic phrases. In this paper, we study the problem of automatically identifying idiomatic dictio- nary entries with such resources. We train an idiom classifier on a newly gathered cor- pus of over 60,000 Wiktionary multi-word definitions, incorporating features …...

In this paper, we present a recursive neural network (RNN) model that works on a syn- tactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task. We also propose to average param- eters in training. …...

This paper proposes a novel approach for rela- tion extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on scoring functions that operate by learning low-dimensional embeddings of words, enti- ties and relationships from a knowledge base. …...

We extend Zhao and Ng's (2007) Chinese anaphoric zero pronoun resolver by (1) using a richer set of features and (2) exploiting the coreference links between zero pronouns dur- ing resolution. Results on OntoNotes show that our approach significantly outperforms two state-of-the-art anaphoric zero pronoun re- solvers. To our knowledge, …...

We present a statistical model for predicting how the user of an interactive, situated NLP system resolved a referring expression. The model makes an initial prediction based on the meaning of the utterance, and revises it con- tinuously based on the user’s behavior. The combined model outperforms its components in …...

We investigate the value-add of topic model- ing in text analysis for depression, and for neu- roticism as a strongly associated personality measure. Using Pennebaker’s Linguistic In- quiry and Word Count (LIWC) lexicon to pro- vide baseline features, we show that straight- forward topic modeling using Latent Dirich- let Allocation …...

Hot trends are likely to bring new business opportunities. For example, “Air Pollution” might lead to a significant increase of the sales of related products, e.g., mouth mask. For e- commerce companies, it is very important to make rapid and correct response to these hot trends in order to improve …...

We propose a Laplacian structured sparsity model to study computational branding ana- lytics. To do this, we collected customer re- views from Starbucks, Dunkin’ Donuts, and other coffee shops across 38 major cities in the Midwest and Northeastern regions of USA. We study the brand related language use through these …...

Automatically clustering words from a mono- lingual or bilingual training corpus into classes is a widely used technique in statisti- cal natural language processing. We present a very simple and easy to implement method for using these word classes to improve trans- lation quality. It can be applied across differ- …...

Topic segmentation classically relies on one of two criteria, either finding areas with co- herent vocabulary use or detecting discontinu- ities. In this paper, we propose a segmenta- tion criterion combining both lexical cohesion and disruption, enabling a trade-off between the two. We provide the mathematical formu- lation of the …...