Articles

Traditional computational approaches to re- ferring expression generation operate in a de- liberate manner, choosing the attributes to be included on the basis of their ability to dis- tinguish the intended referent from its dis- tractors. However, work in psycholinguis- tics suggests that speakers align their refer- ring expressions with …...

Previous work has shown that high quality phrasal paraphrases can be extracted from bilingual parallel corpora. However, it is not clear whether bitexts are an appropriate re- source for extracting more sophisticated sen- tential paraphrases, which are more obviously learnable from monolingual parallel corpora. We extend bilingual paraphrase extraction to …...

Machine-produced text often lacks grammat- icality and fluency. This paper studies gram- maticality improvement using a syntax-based algorithm based on CCG. The goal of the search problem is to find an optimal parse tree among all that can be constructed through se- lection and ordering of the input words. The …...

In this paper, we propose a novel approach to automatic generation of aspect-oriented sum- maries from multiple documents. We first de- velop an event-aspect LDA model to cluster sentences into aspects. We then use extend- ed LexRank algorithm to rank the sentences in each cluster. We use Integer Linear Pro- …...

We propose an efficient way to train maximum entropy language models (MELM) and neural network language models (NNLM). The ad- vantage of the proposed method comes from a more robust and efficient subsampling tech- nique. The original multi-class language mod- eling problem is transformed into a set of bi- nary …...

A re-scoring strategy is proposed that makes it feasible to capture more long-distance de- pendencies in the natural language. Two pass strategies have become popular in a num- ber of recognition tasks such as ASR (au- tomatic speech recognition), MT (machine translation) and OCR (optical character recog- nition). The first …...

Reranking models have been successfully ap- plied to many tasks of Natural Language Pro- cessing. However, there are two aspects of this approach that need a deeper investiga- tion: (i) Assessment of hypotheses generated for reranking at classification phase: baseline models generate a list of hypotheses and these are used …...

In this paper, we introduce a connotation lex- icon, a new type of lexicon that lists words with connotative polarity, i.e., words with pos- itive connotation (e.g., award, promotion) and words with negative connotation (e.g., cancer, war). Connotation lexicons differ from much studied sentiment lexicons: the latter concerns words that …...

Traditional approaches to sentiment classifica- tion rely on lexical features, syntax-based fea- tures or a combination of the two. We pro- pose semantic features using word senses for a supervised document-level sentiment classi- fier. To highlight the benefit of sense-based features, we compare word-based representa- tion of documents with a …...

An entity in a dialogue may be old, new, or mediated/inferrable with respect to the hearer’s beliefs. Knowing the information status of the entities participating in a dia- logue can therefore facilitate its interpreta- tion. We address the under-investigated prob- lem of automatically determining the informa- tion status of discourse …...

This paper investigates novel methods for in- corporating syntactic information in proba- bilistic latent variable models of lexical choice and contextual similarity. The resulting mod- els capture the effects of context on the inter- pretation of a word and in particular its effect on the appropriateness of replacing that word …...

A central topic in natural language process- ing is the design of lexical and syntactic fea- tures suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical simi- larities. We define efficient and powerful ker- nels …...

Most previous research on verb clustering has focussed on acquiring flat classifications from corpus data, although many manually built classifications are taxonomic in nature. Also Natural Language Processing (NLP) applica- tions benefit from taxonomic classifications because they vary in terms of the granularity they require from a classification. We intro- …...

This paper presents a novel method for the com- putation of word meaning in context. We make use of a factorization model in which words, to- gether with their window-based context words and their dependency relations, are linked to latent dimensions. The factorization model al- lows us to determine which …...

Measuring semantic relatedness between words or concepts is a crucial process to many Natural Language Processing tasks. Exiting methods exploit semantic evidence from a single knowledge source, and are predominantly evaluated only in the general domain. This paper introduces a method of harnessing different knowledge sources under a uniform model …...

We re-investigate the rationale for and the ef- fectiveness of adopting the notions of depth and density in WordNet-based semantic sim- ilarity measures. We show that the intuition for including these notions in WordNet-based similarity measures does not always stand up to empirical examination. In particular, the traditional definitions of …...

Metonymic language is a pervasive phe- nomenon. Metonymic type shifting, or ar- gument type coercion, results in a selectional restriction violation where the argument’s se- mantic class differs from the class the predi- cate expects. In this paper we present an un- supervised method that learns the selectional restriction of …...

Lexical co-occurrence is an important cue for detecting word associations. We propose a new measure of word association based on a new notion of statistical significance for lex- ical co-occurrences. Existing measures typ- ically rely on global unigram frequencies to determine expected co-occurrence counts. In- stead, we focus only on …...

Extracting biomedical events from literature has attracted much recent attention. The best- performing systems so far have been pipelines of simple subtask-specific local classifiers. A natural drawback of such approaches are cas- cading errors introduced in early stages of the pipeline. We present three joint models of increasing complexity designed …...

This paper investigates improving supervised word segmentation accuracy with unlabeled data. Both large-scale in-domain data and small-scale document text are considered. We present a unified solution to include features derived from unlabeled data to a discrimina- tive learning model. For the large-scale data, we derive string statistics from Gigaword to …...