Articles

In timeline extraction the goal is to order all the events in which a target entity is involved in a timeline. Due to the lack of explic- itly annotated data, previous work is primar- ily rule-based and uses pre-trained temporal linking systems. In this work, we propose a distantly supervised …...

The problem of noisy and unbalanced train- ing data for supervised keyphrase extraction results from the subjectivity of keyphrase as- signment, which we quantify by crowdsourc- ing keyphrases for news and fashion magazine articles with many annotators per document. We show that annotators exhibit substantial disagreement, meaning that single annotator …...

This paper explores the task of translating nat- ural language queries into regular expressions which embody their meaning. In contrast to prior work, the proposed neural model does not utilize domain-specific crafting, learning to translate directly from a parallel corpus. To fully explore the potential of neural mod- els, we …...

We explore the use of the orthographic syl- lable, a variable-length consonant-vowel se- quence, as a basic unit of translation between related languages which use abugida or alpha- betic scripts. We show that orthographic sylla- ble level translation significantly outperforms models trained over other basic units (word, morpheme and character) …...

An important aspect for the task of grammat- ical error correction (GEC) that has not yet been adequately explored is adaptation based on the native language (L1) of writers, despite the marked influences of L1 on second lan- guage (L2) writing. In this paper, we adapt a neural network joint …...

Technical writing in professional environ- ments, such as user manual authoring, re- quires the use of uniform language. Non- uniform language detection is a novel task, which aims to guarantee the consistency for technical writing by detecting sentences in a document that are intended to have the same meaning within …...

Traditional automated essay scoring systems rely on carefully designed features to evaluate and score essays. The performance of such systems is tightly bound to the quality of the underlying features. However, it is laborious to manually design the most informative fea- tures for such a system. In this paper, we …...

The problem of accurately predicting rela- tive reading difficulty across a set of sen- tences arises in a number of important natu- ral language applications, such as finding and curating effective usage examples for intelli- gent language tutoring systems. Yet while significant research has explored document- and passage-level reading difficulty, …...

A natural language interface to answers on the Web can help us access information more ef- ficiently. We start with an interesting source of information—infoboxes in Wikipedia that summarize factoid knowledge—and develop a comprehensive approach to answering ques- tions with high precision. We first build a system to access data …...

We operate a change of paradigm and hy- pothesize that keywords are more likely to be found among influential nodes of a graph-of- words rather than among its nodes high on eigenvector-related centrality measures. To test this hypothesis, we introduce unsuper- vised techniques that capitalize on graph de- generacy. Our …...

Text reuse refers to citing, copying or allud- ing text excerpts from a text resource to a new context. While detecting reuse in con- temporary languages is well supported—given extensive research, techniques, and corpora— automatically detecting historical text reuse is much more difficult. Corpora of historical lan- guages are less …...

Regularization is a critical step in supervised learning to not only address overfitting, but also to take into account any prior knowledge we may have on the features and their de- pendence. In this paper, we explore state- of-the-art structured regularizers and we pro- pose novel ones based on clusters …...

We introduce an online popularity prediction and tracking task as a benchmark task for rein- forcement learning with a combinatorial, nat- ural language action space. A specified num- ber of discussion threads predicted to be pop- ular are recommended, chosen from a fixed window of recent comments to track. Novel …...

Existing work on detecting deceptive reviews primarily focuses on feature engineering and applies off-the-shelf supervised classification algorithms to the problem. Then, one real challenge would be to manually recognize plentiful ground truth spam review data for model building, which is rather difficult and often requires domain expertise in practice. In …...

Users prefer natural language software re- quirements because of their usability and ac- cessibility. When they describe their wishes for software development, they often provide off-topic information. We therefore present REaCT1, an automated approach for identify- ing and semantically annotating the on-topic parts of requirement descriptions. It is de- signed …...

Sequences found at the beginning of TV shows help the audience absorb the essence of previous episodes, and grab their attention with upcoming plots. In this paper, we pro- pose a novel task, text recap extraction. Com- pared with conventional summarization, text recap extraction captures the duality of sum- marization …...

We present a shift-reduce CCG semantic parser. Our parser uses a neural network ar- chitecture that balances model capacity and computational cost. We train by transferring a model from a computationally expensive log- linear CKY parser. Our learner addresses two challenges: selecting the best parse for learn- ing when the …...

For many NLP applications that require a parser, the sentences of interest may not be well-formed. If the parser can overlook prob- lems such as grammar mistakes and produce a parse tree that closely resembles the correct analysis for the intended sentence, we say that the parser is robust. This …...

We describe a neural shift-reduce parsing model for CCG, factored into four unidirec- tional LSTMs and one bidirectional LSTM. This factorization allows the linearization of the complete parsing history, and results in a highly accurate greedy parser that outper- forms all previous beam-search shift-reduce parsers for CCG. By further deriving …...

We introduce two first-order graph-based de- pendency parsers achieving a new state of the art. The first is a consensus parser built from an ensemble of independently trained greedy LSTM transition-based parsers with different random initializations. We cast this approach as minimum Bayes risk decoding (under the Hamming cost) and …...