Articles

We present a simple, data-driven approach to generation from knowledge bases (KB). A key feature of this approach is that grammar induction is driven by the ex- tended domain of locality principle of TAG (Tree Adjoining Grammar); and that it takes into account both syntactic and semantic information. The resulting …...

We present an incremental joint frame- work to simultaneously extract entity men- tions and relations using structured per- ceptron with efficient beam-search. segment-based decoder based on the ideaA of semi-Markov chain is adopted to the new framework as opposed to traditional token-based tagging. In addition, by virtue of the inexact …...

We investigate whether parsers can be used for self-monitoring in surface real- ization in order to avoid egregious errors involving “vicious” ambiguities, namely those where the intended interpretation fails to be considerably more likely than alternative ones. Using parse accuracy in a simple reranking strategy for self- monitoring, we find …...

In order to extract entities of a fine-grained category from semi-structured data in web pages, existing information extraction sys- tems rely on seed examples or redundancy across multiple web pages. In this paper, we consider a new zero-shot learning task of extracting entities specified by a natural language query (in …...

Wikification for tweets aims to automat- ically identify each concept mention in a tweet and link it to a concept referent in a knowledge base (e.g., Wikipedia). Due to the shortness of a tweet, a collective inference model incorporating global ev- idence from multiple mentions and con- cepts is more …...

We consider the problem of automatically inferring latent character types in a collec- tion of 15,099 English novels published between 1700 and 1899. Unlike prior work in which character types are assumed responsible for probabilistically generat- ing all text associated with a character, we introduce a model that employs mul- …...

Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expec- tation maximization. However, these new methods lack the rich priors associated with probabilistic models. We examine Arora et al.’s anchor words algorithm for topic mod- eling and develop new, regularized algo- rithms that not only mathematically resem- ble …...

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the is- sue, several knowledge-based models have been proposed to incorporate prior knowl- edge provided by the user to guide mod- eling. …...

Product feature mining is a key subtask in fine-grained opinion mining. Previ- ous works often use syntax constituents in this task. However, syntax-based methods can only use discrete contextual informa- tion, which may suffer from data sparsity. This paper proposes a novel product fea- ture mining method which leverages lexi- …...

This paper proposes a novel context-aware method for analyzing sentiment at the level of individual sentences. Most ex- isting machine learning approaches suf- fer from limitations in the modeling of complex linguistic structures across sen- tences and often fail to capture non- local contextual cues that are important for sentiment …...

Extracting opinion targets and opinion words from online reviews are two fun- damental tasks in opinion mining. This paper proposes a novel approach to col- lectively extract them with graph co- ranking. First, compared to previous methods which solely employed opinion relations among words, our method con- structs a heterogeneous …...

Negation words, such as no and not, play a fundamental role in modifying sentiment of textual expressions. We will refer to a negation word as the negator and the text span within the scope of the negator as the argument. Commonly used heuristics to estimate the sentiment of negated expres- …...

Inspired by experimental psychological findings suggesting that function words play a special role in word learning, we make a simple modification to an Adaptor Grammar based Bayesian word segmenta- tion model to allow it to learn sequences of monosyllabic “function words” at the beginnings and endings of collocations of (possibly …...

We present an approach for automatically learning to solve algebra word problems. Our algorithm reasons across sentence boundaries to construct and solve a sys- tem of linear equations, while simultane- ously recovering an alignment of the vari- ables and numbers in these equations to the problem text. The learning algorithm …...

Unsupervised word sense disambiguation (WSD) methods are an attractive approach to all-words WSD due to their non-reliance on expensive annotated data. Unsuper- vised estimates of sense frequency have been shown to be very useful for WSD due to the skewed nature of word sense distri- butions. This paper presents a …...

We show that it is possible to reliably dis- criminate whether a syntactic construction is meant literally or metaphorically using lexical semantic features of the words that participate in the construction. Our model is constructed using English resources, and we obtain state-of-the-art performance relative to previous work in this language. …...

Context-predicting models (more com- monly known as embeddings or neural language models) are the new kids on the distributional semantics block. Despite the buzz surrounding these models, the litera- ture is still lacking a systematic compari- son of the predictive models with classic, count-vector-based distributional semantic approaches. In this paper, …...

We present a parser that relies primar- ily on extracting information directly from surface spans rather than on propagat- ing information through enriched gram- mar structure. For example, instead of cre- ating separate grammar symbols to mark the definiteness of an NP, our parser might instead capture the same information …...

Recently, neural network models for nat- ural language processing tasks have been increasingly focused on for their ability to alleviate the burden of manual feature engineering. In this paper, we propose a novel neural network model for Chinese word segmentation called Max-Margin Tensor Neural Network (MMTNN). By exploiting tag embeddings …...

This paper presents the first dependency model for a shift-reduce CCG parser. Mod- elling dependencies is desirable for a num- ber of reasons, including handling the “spurious” ambiguity of CCG; fitting well with the theory of CCG; and optimizing for structures which are evaluated at test time. We develop a …...