Articles

Word clusters are useful for many NLP tasks including training neural network language models, but current increases in datasets are outpacing the ability of word clusterers to han- dle them. Little attention has been paid thus far on inducing high-quality word clusters at a large scale. The predictive exchange algo- …...

Recent work across several AI subdisciplines has focused on automatically solving math word problems. In this paper we introduce MAWPS, an online repository of Math Word Problems, to provide a unified testbed to eval- uate different algorithms. MAWPS allows for the automatic construction of datasets with particular characteristics, providing tools …...

This paper proposes an incremental learning strategy for neural word embedding methods, such as SkipGrams and Global Vectors. Since our method iteratively generates embedding vectors one dimension at a time, obtained vec- tors equip a unique property. Namely, any right-truncated vector matches the solution of the corresponding lower-dimensional embed- ding. …...

Quotations are kernels not just of wisdom but also of beautiful and striking language. While recent studies have characterized the stylistic features of quotations, we characterize the or- der of stylistic information within quotations. Analyzing a corpus of two-sentence quota- tions collected from the social network Tum- blr, we explore …...

Research on grammatical error correction has received considerable attention. For dealing with all types of errors, grammatical error cor- rection methods that employ statistical ma- chine translation (SMT) have been proposed in recent years. An SMT system generates candidates with scores for all candidates and selects the sentence with the …...

Most work on extracting parallel text from comparable corpora depends on linguistic re- sources such as seed parallel documents or translation dictionaries. This paper presents a simple baseline approach for bootstrapping a parallel collection. It starts by observing doc- uments published on similar dates and the co- occurrence of a …...

The Lexical Substitution task involves select- ing and ranking lexical paraphrases for a target word in a given sentential context. We present PIC, a simple measure for estimating the ap- propriateness of substitutes in a given con- text. PIC outperforms another simple, com- parable model proposed in recent work, es- …...

Previous work on Automatic Paraphrase Iden- tification (PI) is mainly based on modeling text similarity between two sentences. In con- trast, we study methods for automatically de- tecting whether a text fragment only appear- ing in a sentence of the evaluated sentence pair is important or ancillary information with re- …...

Part-of-speech (POS) taggers trained on newswire perform much worse on domains such as subtitles, lyrics, or tweets. In addition, these domains are also heterogeneous, e.g., with respect to registers and dialects. In this paper, we consider the problem of learning a POS tagger for subtitles, lyrics, and tweets associated with …...

We explore the consequences of representing token segmentations as hierarchical structures (trees) for the task of Multiword Expression (MWE) recognition, in isolation or in com- bination with dependency parsing. We pro- pose a novel representation of token segmen- tation as trees on tokens, resembling depen- dency trees. Given this new …...

This work addresses the task of dependency labeling—assigning labels to an (unlabeled) dependency tree. We employ and extend a feature representation learning approach, op- timizing it for both high speed and accu- racy. We apply our labeling model on top of state-of-the-art parsers and evaluate its perfor- mance on standard …...

Poorly translated text is often disfluent and difficult to read. In contrast, well-formed translations require less time to process. In this paper, we model the differences in reading patterns of Machine Translation (MT) evalua- tors using novel features extracted from their gaze data, and we learn to predict the qual- …...

Automated geolocation of social media mes- sages can benefit a variety of downstream ap- plications. However, these geolocation sys- tems are typically evaluated without attention to how changes in time impact geolocation. Since different people, in different locations write messages at different times, these factors can significantly vary the performance …...

This paper presents an study of the use of in- terlocking phrases in phrase-based statistical machine translation. We examine the effect on translation quality when the translation units used in the translation hypotheses are allowed to overlap on the source side, on the target side and on both sides. A …...

We present a fast, simple, and high-accuracy short answer grading system. Given a short- answer question and its correct answer, key measures of the correctness of a student re- sponse can be derived from its semantic sim- ilarity with the correct answer. Our super- vised model (1) utilizes recent advances …...

In this paper, we explore sentiment composi- tion in phrases that have at least one positive and at least one negative word—phrases like happy accident and best winter break. We compiled a dataset of such opposing polar- ity phrases and manually annotated them with real-valued scores of sentiment association. Using …...

In this paper, we present a straightforward strategy for transferring dependency parsers across languages. The proposed method learns a parser from partially annotated data obtained through the projection of annotations across unambiguous word alignments. It does not rely on any modeling of the reliability of dependency and/or alignment links and …...

Given a pair of sentences, we present com- putational models to assess if one sentence is simpler to read than the other. While ex- isting models explored the usage of phrase structure features using a non-incremental parser, experimental evidence suggests that the human language processor works incre- mentally. We empirically …...

Recently, researchers have demonstrated that both Chinese word and its component charac- ters provide rich semantic information when learning Chinese word embeddings. Howev- er, they ignored the semantic similarity across component characters in a word. In this paper, we learn the semantic contribution of charac- ters to a word by …...

We provide the first extensive evaluation of how using different types of context to learn skip-gram word embeddings affects perfor- mance on a wide range of intrinsic and ex- trinsic NLP tasks. Our results suggest that while intrinsic tasks tend to exhibit a clear preference to particular types of contexts …...