Articles

We present a novel approach, called selec- tional branching, which uses confidence es- timates to decide when to employ a beam, providing the accuracy of beam search at speeds close to a greedy transition-based dependency parsing approach. Selectional branching is guaranteed to perform a fewer number of transitions than beam …...

Probabilistic context-free grammars have the unusual property of not always defining tight distributions (i.e., the sum of the “probabili- ties” of the trees the grammar generates can be less than one). This paper reviews how this non-tightness can arise and discusses its im- pact on Bayesian estimation of PCFGs. We …...

We propose a new optimization frame- work for summarization by generalizing the submodular framework of (Lin and Bilmes, 2011). In our framework the sum- marization desideratum is expressed as a sum of a submodular function and a non- submodular function, which we call dis- persion; the latter uses inter-sentence dis- …...

This study proposes a text summarization model that simultaneously performs sen- tence extraction and compression. We translate the text summarization task into a problem of extracting a set of depen- dency subtrees in the document cluster. We also encode obligatory case constraints as must-link dependency constraints in or- der to …...

In this paper, we propose a bigram based supervised method for extractive docu- ment summarization in the integer linear programming (ILP) framework. For each bigram, a regression model is used to es- timate its frequency in the reference sum- mary. The regression model uses a vari- ety of indicative features …...

During real-life interactions, people are naturally gesturing and modulating their voice to emphasize specific points or to express their emotions. With the recent growth of social websites such as YouTube, Facebook, and Amazon, video reviews are emerging as a new source of multimodal and natural opinions that has been left …...

Social Media contain a multitude of user opinions which can be used to predict real- world phenomena in many domains in- cluding politics, finance and health. Most existing methods treat these problems as linear regression, learning to relate word frequencies and other simple features to a known response variable (e.g., …...

Sentiment Similarity of word pairs reflects the distance between the words regarding their underlying sentiments. This paper aims to in- fer the sentiment similarity between word pairs with respect to their senses. To achieve this aim, we propose a probabilistic emotion- based approach that is built on a hidden emo- …...

While there have been many attempts to estimate the emotion of an addresser from her/his utterance, few studies have ex- plored how her/his utterance affects the emotion of the addressee. This has mo- tivated us to investigate two novel tasks: predicting the emotion of the addressee and generating a response …...

A number of different notions, including subjectivity, have been proposed for dis- tinguishing parts of documents that con- vey sentiment from those that do not. We propose a new concept, sentiment rele- vance, to make this distinction and argue that it better reflects the requirements of sentiment analysis systems. We …...

We present the first unsupervised ap- proach for semantic parsing that rivals the accuracy of supervised approaches in translating natural-language questions to database queries. Our GUSP system produces a semantic parse by annotat- ing the dependency-tree nodes and edges with latent states, and learns a proba- bilistic grammar using EM. …...

It is important that the testimony of chil- dren be admissible in court, especially given allegations of abuse. Unfortunately, children can be misled by interrogators or might offer false information, with dire consequences. In this work, we evalu- ate various parameterizations of five clas- sifiers (including support vector machines, neural …...

Hyperedge replacement grammar (HRG) is a formalism for generating and trans- forming graphs that has potential appli- cations in natural language understand- ing and generation. A recognition al- gorithm due to Lautemann is known to be polynomial-time for graphs that are connected and of bounded degree. We present a more …...

Given that structured output prediction is typically performed over entire datasets, one natural question is whether it is pos- sible to re-use computation from earlier inference instances to speed up inference for future instances. Amortized inference has been proposed as a way to accomplish this. In this paper, first, we …...

Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is a funda- mental task of Natural Language Process- ing. In this paper we draw upon recent advances in the learning of vector space representations of sentential semantics and the transparent interface …...

This paper proposes a novel smoothing model with a combinatorial optimization scheme for all-words word sense disam- biguation from untagged corpora. By gen- eralizing discrete senses to a continuum, we introduce a smoothing in context-sense space to cope with data-sparsity result- ing from a large variety of linguistic con- text …...

Semantic frames are a rich linguistic re- source. There has been much work on semantic frame parsers, but less that applies them to general NLP problems. We address a task to predict change in stock price from financial news. Seman- tic frames help to generalize from spe- cific sentences to …...

Community question answering (CQA) has become an increasingly popular re- search topic. In this paper, we focus on the problem of question retrieval. Question retrieval in CQA can automatically find the most relevant and recent questions that have been solved by other users. However, the word ambiguity and word mismatch …...

Subcategorization frames (SCFs), selec- tional preferences (SPs) and verb classes capture related aspects of the predicate- argument structure. We present the first unified framework for unsupervised learn- ing of these three types of information. We show how to utilize Determinantal Point Processes (DPPs), elegant proba- bilistic models that are defined …...

Finding concepts in natural language ut- terances is a challenging task, especially given the scarcity of labeled data for learn- ing semantic ambiguity. Furthermore, data mismatch issues, which arise when the expected test (target) data does not exactly match the training data, aggra- vate this scarcity problem. To deal with …...