Articles

Recently, there has been increasing interest in learning semantic parsers with indirect super- vision, but existing work focuses almost ex- clusively on question answering. Separately, there have been active pursuits in leveraging databases for distant supervision in informa- tion extraction, yet such methods are often limited to binary relations and …...

Named entity recognition (NER) systems trained on newswire perform very badly when tested on Twitter. Signals that were reliable in copy-edited text disappear almost entirely in Twitter’s informal chatter, requiring the con- struction of specialized models. Using well- understood techniques, we set out to improve Twitter NER performance when given …...

This paper studies how to incorporate the ex- ternal word correlation knowledge to improve the coherence of topic modeling. Existing topic models assume words are generated in- dependently and lack the mechanism to utilize the rich similarity relationships among words to learn coherent topics. To solve this prob- lem, we …...

Crowdsourcing makes it possible to create translations at much lower cost than hiring professional translators. However, it is still expensive to obtain the millions of transla- tions that are needed to train statistical ma- chine translation systems. We propose two mechanisms to reduce the cost of crowdsourc- ing while maintaining …...

Sentiment analysis has undergone a shift from document-level analysis, where labels ex- presses the sentiment of a whole document or whole sentence, to subsentential approaches, which assess the contribution of individual phrases, in particular including the composi- tion of sentiment terms and phrases such as negators and intensifiers. Starting from …...

Representation learning is the dominant tech- nique for unsupervised domain adaptation, but existing approaches have two major weak- nesses. First, they often require the spec- ification of “pivot features” that generalize across domains, which are selected by task- specific heuristics. We show that a novel but simple feature embedding approach …...

Words are polysemous. However, most ap- proaches to representation learning for lexical semantics assign a single vector to every sur- face word type. Meanwhile, lexical ontologies such as WordNet provide a source of com- plementary knowledge to distributional infor- mation, including a word sense inventory. In this paper we propose …...

We define a restricted class of non-projective trees that 1) covers many natural language sentences; and 2) can be parsed exactly with a generalization of the popular arc-eager sys- tem for projective trees (Nivre, 2003). Cru- cially, this generalization only adds constant overhead in run-time and space keeping the parser’s …...

We present a self-training approach to unsu- pervised dependency parsing that reuses exist- ing supervised and unsupervised parsing algo- rithms. Our approach, called ‘iterated rerank- ing’ (IR), starts with dependency trees gener- ated by an unsupervised parser, and iteratively improves these trees using the richer proba- bility models used in …...

We investigate the problem of predicting the quality of automatic speech recognition (ASR) output under the following rigid con- straints: i) reference transcriptions are not available, ii) confidence information about the system that produced the transcriptions is not accessible, and iii) training and test data come from multiple domains. To …...

We introduce a distantly supervised event ex- traction approach that extracts complex event templates from microblogs. We show that this near real-time data source is more challeng- ing than news because it contains information that is both approximate (e.g., with values that are close but different from the gold truth) …...

This study tackles the problem of paraphrase acquisition: achieving high coverage as well as accuracy. Our method first induces para- phrase patterns from given seed paraphrases, exploiting the generality of paraphrases exhib- ited by pairs of lexical variants, e.g., “amend- ment” and “amending,” in a fully empiri- cal way. It …...

A key challenge of designing coherent seman- tic ontology for spoken language understand- ing is to consider inter-slot relations. In prac- tice, however, it is difficult for domain experts and professional annotators to define a coher- ent slot set, while considering various lexi- cal, syntactic, and semantic dependencies. In this …...

We present Model Invertibility Regularization (MIR), a method that jointly trains two direc- tional sequence alignment models, one in each direction, and takes into account the invertibil- ity of the alignment task. By coupling the two models through their parameters (as opposed to through their inferences, as in Liang et …...

Linguistic borrowing is the phenomenon of transferring linguistic constructions (lexical, phonological, morphological, and syntactic) from a “donor” language to a “recipient” lan- guage as a result of contacts between commu- nities speaking different languages. Borrowed words are found in all languages, and—in con- trast to cognate relationships—borrowing rela- tionships may …...

Research on ranked retrieval of spoken con- tent has assumed the existence of some auto- mated (word or phonetic) transcription. Re- cently, however, methods have been demon- strated for matching spoken terms to spoken content without the need for language-tuned transcription. This paper describes the first application of such techniques …...

We present a novel evaluation method for grammatical error correction that addresses problems with previous approaches and scores systems in terms of improvement on the orig- inal text. Our method evaluates corrections at the token level using a globally optimal align- ment between the source, a system hypothesis, and a …...

Multiview LSA (MVLSA) is a generalization of Latent Semantic Analysis (LSA) that sup- ports the fusion of arbitrary views of data and relies on Generalized Canonical Correlation Analysis (GCCA). We present an algorithm for fast approximate computation of GCCA, which when coupled with methods for han- dling missing values, is …...

The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natu- ral Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic re- sources, such as WordNet, or on encyclope- dic resources, such as Wikipedia. We …...

Analyzing public opinions towards products, services and social events is an important but challenging task. An accurate sentiment an- alyzer should take both lexicon-level infor- mation and corpus-level information into ac- count. It also needs to exploit the domain- specific knowledge and utilize the common knowledge shared across domains. In …...