Articles

Semantic inference is a core component of many natural language applications. In re- sponse, several researchers have developed algorithms for automatically learning infer- ence rules from textual corpora. However, these rules are often either imprecise or un- derspecified in directionality. In this paper we propose an algorithm called LEDIR that …...

Inclusions from other languages can be a significant source of errors for monolin- gual parsers. We show this for English in- clusions, which are sufficiently frequent to present a problem when parsing German. We describe an annotation-free approach for accurately detecting such inclusions, and de- velop two methods for interfacing …...

This paper compares a deep and a shallow processing approach to the problem of clas- sifying a sentence as grammatically well- formed or ill-formed. The deep processing approach uses the XLE LFG parser and En- glish grammar: two versions are presented, one which uses the XLE directly to perform the …...

We present a comparative error analysis of the two dominant approaches in data- driven dependency parsing: global, exhaus- tive, graph-based models, and local, greedy, transition-based models. We show that, in spite of similar performance overall, the two models produce different types of errors, in a way that can be explained …...

A notable gap in research on statistical de- pendency parsing is a proper conditional probability distribution over nonprojective dependency trees for a given sentence. We exploit the Matrix Tree Theorem (Tutte, 1984) to derive an algorithm that efficiently sums the scores of all nonprojective trees in a sentence, permitting the …...

Approaches to plural reference generation emphasise descriptive brevity, but often lack empirical backing. This paper describes a corpus-based study of plural descrip- tions, and proposes a psycholinguistically- motivated algorithm for plural reference generation. The descriptive strategy is based on partitioning and incorporates corpus- derived heuristics. An exhaustive evaluation shows that …...

Many emerging applications require doc- uments to be repeatedly updated. Such documents include newsfeeds, webpages, and shared community resources such as Wikipedia. In this paper we address the task of inserting new information into exist- ing texts. In particular, we wish to deter- mine the best location in a text …...

This paper presents a tree-to-tree transduc- tion method for text rewriting. Our model is based on synchronous tree substitution grammar, a formalism that allows local dis- tortion of the tree topology and can thus naturally capture structural mismatches. We describe an algorithm for decoding in this framework and show how …...

This paper provides an algorithmic frame- work for learning statistical models involv- ing directed spanning trees, or equivalently non-projective dependency structures. We show how partition functions and marginals for directed spanning trees can be computed by an adaptation of Kirchhoff’s Matrix-Tree Theorem. To demonstrate an application of the method, we …...

We show for the first time that incorporating the predictions of a word sense disambigua- tion system within a typical phrase-based statistical machine translation (SMT) model consistently improves translation quality across all three different IWSLT Chinese- English test sets, as well as producing sta- tistically significant improvements on the larger …...

We describe an approach to improve Statistical Machine Translation (SMT) performance using multi-lingual, parallel, sentence-aligned corpora in several bridge languages. Our approach consists of a sim- ple method for utilizing a bridge language to create a word alignment system and a proce- dure for combining word alignment systems from multiple …...

Word alignment is the problem of annotating parallel text with translational correspon- dence. Previous generative word alignment models have made structural assumptions such as the 1-to-1, 1-to-N, or phrase-based consecutive word assumptions, while previ- ous discriminative models have either made such an assumption directly or used features derived from a …...

Previous machine learning techniques for answer selection in question answering (QA) have required question-answer train- ing pairs. It has been too expensive and labor-intensive, however, to collect these training pairs. This paper presents a novel unsupervised support vector machine (U- SVM) classifier for answer selection, which is independent of language …...

Shallow semantic parsing, the automatic identification and labeling of sentential con- stituents, has recently received much atten- tion. Our work examines whether seman- tic role information is beneficial to question answering. We introduce a general frame- work for answer extraction which exploits semantic role annotations in the FrameNet paradigm. We …...

This paper presents a syntax-driven ap- proach to question answering, specifically the answer-sentence selection problem for short-answer questions. Rather than us- ing syntactic features to augment exist- ing statistical classifiers (as in previous work), we build on the idea that ques- tions and their (correct) answers relate to each other …...

Sentence compression holds promise for many applications ranging from summarisa- tion to subtitle generation and subtitle gen- eration. The task is typically performed on isolated sentences without taking the sur- rounding context into account, even though most applications would operate over entire documents. In this paper we present a dis- …...

Question answering systems provide an elegant way for people to access an underlying knowledge base. Humans are not only interested in factual questions but also interested in opinions. This paper deals with question analysis and answer passage retrieval in opinion QA systems. For question analysis, six opinion question types are …...

We introduce a new method for automatically disambiguation of word translations by using dependency relationships. In our approach, we learn the relationships between translations and dependency relationships from a parallel corpus. The method consists of a training stage and a runtime stage. During the training stage, the system automatically learns …...

In order to train machines to ‘understand’ natural language, we proposed a universal concept representational mechanism called E-HowNet to encode lexical semantics. In this paper, we take interrogative constructions as examples, i.e. concepts or sentences for asking questions or making inquiries, to demonstrate the mechanisms of semantic representation and composition …...

An effective method based on GMM is proposed in this paper for speech emotional recognition; a compensation transformation is introduced in the recognition stage to reduce the influence of variations in speech characteristics and noise. The extraction of emotional features includes the globe feature, time series structure feature, LPCC, MFCC …...