Articles

Access to word–sentiment associations is use- ful for many applications, including senti- ment analysis, stance detection, and linguistic analysis. However, manually assigning fine- grained sentiment association scores to words has many challenges with respect to keeping annotations consistent. We apply the annota- tion technique of Best–Worst Scaling to ob- tain …...

We present an approach to the new task of opinion holder and target extraction on opin- ion compounds. Opinion compounds (e.g. user rating or victim support) are noun com- pounds whose head is an opinion noun. We do not only examine features known to be ef- fective for noun compound …...

This paper presents a clustering approach that simultaneously identifies product features and groups them into aspect categories from on- line reviews. Unlike prior approaches that first extract features and then group them into cat- egories, the proposed approach combines fea- ture and aspect discovery instead of chaining them. In addition, …...

We examine different features and classifiers for the categorization of opinion words into actor and speaker view. To our knowledge, this is the first comprehensive work to address sentiment views on the word level taking into consideration opinion verbs, nouns and adjec- tives. We consider many high-level features requiring only …...

Embeddings are generic representations that are useful for many NLP tasks. In this paper, we introduce DENSIFIER, a method that learns an orthogonal transformation of the embed- ding space that focuses the information rele- vant for a task in an ultradense subspace of a dimensionality that is smaller by a …...

In this paper, we examine the impact of employing contextual, structural information from a tree-structured document set to derive a language model. Our results show that this in- formation significantly improves the accuracy of the resultant model. 1...

We present a corpus of 2,380 natural language queries paired with machine readable formu- lae that can be executed against world wide geographic data of the OpenStreetMap (OSM) database. We use the corpus to learn an ac- curate semantic parser that builds the basis of a natural language interface to …...

Meaning Representation using Tree Transducers Jeffrey Flanigan♠ Chris Dyer♠ Noah A. Smith♥ Jaime Carbonell♠ ♠School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA ♥Computer Science & Engineering, University of Washington, Seattle, WA, USA {jflanigan,cdyer,jgc}@cs.cmu.edu, nasmith@cs.washington.edu Abstract Language generation from purely semantic representations is a challenging task. This pa- per …...

We propose an end-to-end, domain- independent neural encoder-aligner-decoder model for selective generation, i.e., the joint task of content selection and surface realization. Our model first encodes a full set of over-determined database event records via an LSTM-based recurrent neural network, then utilizes a novel coarse-to-fine aligner to identify the small …...

This paper introduces a new annotated cor- pus based on an existing informal text corpus: the NUS SMS Corpus (Chen and Kan, 2013). The new corpus includes 76,490 noun phrases from 26,500 SMS messages, annotated by uni- versity students. We then explored several graphical models, including a novel variant of …...

In many languages, sparse availability of re- sources causes numerous challenges for tex- tual analysis tasks. Text classification is one of such standard tasks that is hindered due to lim- ited availability of label information in low- resource languages. Transferring knowledge (i.e. label information) from high-resource to low-resource languages might …...

While neural networks have been successfully applied to many NLP tasks the resulting vector- based models are very difficult to interpret. For example it’s not clear how they achieve compositionality, building sentence meaning from the meanings of words and phrases. In this paper we describe strategies for visual- izing compositionality …...

This paper addresses the problem of comment classification in community Question Answer- ing. Following the state of the art, we ap- proach the task with a global inference pro- cess to exploit the information of all com- ments in the answer-thread in the form of a fully connected graph. Our …...

Accurate automatic processing of Web queries is im- portant for high-quality information retrieval from the Web. While the syntactic structure of a large portion of these queries is trivial, the structure of queries with question intent is much richer. In this paper we therefore address the task of statistical syntactic …...

We present a model of morphological seg- mentation that jointly learns to segment and restore orthographic changes, e.g., funniest �→ fun-y-est. We term this form of analysis canon- ical segmentation and contrast it with the tra- ditional surface segmentation, which segments a surface form into a sequence of substrings, e.g., …...

Many puns create humor through the rela- tionship between a pun and its phonologically similar target. For example, in “Don’t take ge- ologists for granite” the word “granite” is a pun with the target “granted”. The recovery of the target in the mind of the listener is essen- tial to …...

In this paper, we address the task of language- independent, knowledge-lean and unsuper- vised compound splitting, which is an essen- tial component for many natural language pro- cessing tasks such as machine translation. Pre- vious methods on statistical compound split- ting either include language-specific knowl- edge (e.g., linking elements) or …...

How should one apply deep learning to tasks such as morphological reinflection, which stochastically edit one string to get another? A recent approach to such sequence-to-sequence tasks is to compress the input string into a vector that is then used to generate the out- put string, using recurrent neural networks. …...

Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguis- tic transformation. We model the problem of inflection generation as a character sequence to sequence learning problem and present a variant of the neural encoder-decoder model for solving it. Our …...

In this paper we present a word decom- pounding method that is based on distribu- tional semantics. Our method does not re- quire any linguistic knowledge and is initial- ized using a large monolingual corpus. The core idea of our approach is that parts of com- pounds (like “candle” and …...