Articles

We introduce cause identification, a new problem involving classification of in- cident reports in the aviation domain. Specifically, given a set of pre-defined causes, a cause identification system seeks to identify all and only those causes that can explain why the aviation incident de- scribed in a given report occurred. …...

Paraphrase generation (PG) is important in plenty of NLP applications. However, the research of PG is far from enough. In this paper, we propose a novel method for statistical paraphrase generation (SPG), which can (1) achieve various applications based on a uniform statistical model, and (2) naturally combine multiple resources …...

We investigate the influence of informa- tion status (IS) on constituent order in Ger- man, and integrate our findings into a log- linear surface realisation ranking model. We show that the distribution of pairs of IS categories is strongly asymmetric. More- over, each category is correlated with mor- phosyntactic features, …...

This paper addresses the task of handling unknown terms in SMT. We propose us- ing source-language monolingual models and resources to paraphrase the source text prior to translation. We further present a conceptual extension to prior work by al- lowing translations of entailed texts rather than paraphrases only. A method …...

This paper describes log-linear models for a general-purpose sentence realizer based on de- pendency structures. Unlike traditional realiz- ers using grammar rules, our method realizes sentences by linearizing dependency relations directly in two steps. First, the relative order between head and each dependent is deter- mined by their dependency relation. …...

We report in this paper our work on accurately generating case markers and suffixes in English-to-Hindi SMT. Hindi is a relatively free word-order language, and makes use of a comparatively richer set of case markers and morphological suffixes for correct meaning representa- tion. From our experience of large-scale English-Hindi MT, …...

We present a phrasal synchronous gram- mar model of translational equivalence. Unlike previous approaches, we do not aresort to heuristics or constraints from word-alignment model, but instead directly induce a synchronous grammar from parallel sentence-aligned corpora. We use a hierarchical Bayesian prior to bias towards compact grammars with small translation …...

Conventional sentence compression meth- ods employ a syntactic parser to compress a sentence without changing its mean- ing. However, the reference compres- sions made by humans do not always re- tain the syntactic structures of the original sentences. Moreover, for the goal of on- demand sentence compression, the time spent …...

We demonstrate that transformation-based learning can be used to correct noisy speech recognition transcripts in the lec- ture domain with an average word error rate reduction of 12.9%. Our method is distinguished from earlier related work by its robustness to small amounts of training data, and its resulting efficiency, in …...

While OOV is always a problem for most languages in ASR, in the Chinese case the problem can be avoided by utilizing char- acter n-grams and moderate performances can be obtained. However, character n- gram has its own limitation and proper addition of new words can increase the ASR performance. …...

Efficiency is a prime concern in syntactic MT de- coding, yet significant developments in statisti- cal parsing with respect to asymptotic efficiency haven’t yet been explored in MT. Recently, McDonald et al. (2005b) formalized dependency parsing as a maximum spanning tree (MST) prob- lem, which can be solved in quadratic …...

Spontaneously produced speech text often includes disfluencies which make it diffi- cult to analyze underlying structure. Suc- cessful reconstruction of this text would transform these errorful utterances into fluent strings and offer an alternate mech- anism for analysis. Our investigation of naturally-occurring spontaneous speaker errors aligned to corrected text with …...

Opinion Question Answering (Opinion QA), which aims to find the authors’ sen- timental opinions on a specific target, is more challenging than traditional fact- based question answering problems. To extract the opinion oriented answers, we need to consider both topic relevance and opinion sentiment issues. Current solu- tions to this …...

We present a graph-based semi-supervised learning for the question-answering (QA) task for ranking candidate sentences. Us- ing textual entailment analysis, we obtain entailment scores between a natural lan- guage question posed by the user and the candidate sentences returned from search engine. The textual entailment between two sentences is assessed …...

This paper presents and evaluates several original techniques for the latent classifi- cation of biographic attributes such as gen- der, age and native language, in diverse genres (conversation transcripts, email) and languages (Arabic, English). First, we present a novel partner-sensitive model for extracting biographic attributes in con- versations, given the …...

Supervised polarity classification systems are typically domain-specific. Building these systems involves the expensive pro- cess of annotating a large amount of data for each domain. A potential solution to this corpus annotation bottleneck is to build unsupervised polarity classification systems. However, unsupervised learning of polarity is difficult, owing in part …...

We present a series of experiments on au- tomatically identifying the sense of im- plicit discourse relations, i.e. relations that are not marked with a discourse con- nective such as “but” or “because”. We work with a corpus of implicit relations present in newspaper text and report re- sults on …...

In text categorization, feature selection (FS) is a strategy that aims at making text classifiers more efficient and accurate. However, when dealing with a new task, it is still difficult to quickly select a suitable one from various FS methods provided by many previous studies. In this paper, we propose …...

Articles in the Penn TreeBank were iden- tified as being reviews, summaries, let- ters to the editor, news reportage, correc- tions, wit and short verse, or quarterly profit reports. All but the latter three were then characterised in terms of fea- tures manually annotated in the Penn Dis- course TreeBank …...

Monolingual translation probabilities have recently been introduced in retrieval mod- els to solve the lexical gap problem. They can be obtained by training statisti- cal translation models on parallel mono- lingual corpora, such as question-answer pairs, where answers act as the “source” language and questions as the “target” language. In …...