Articles

Dialogue act classification is a central chal- lenge for dialogue systems. Although the im- portance of emotion in human dialogue is widely recognized, most dialogue act classifi- cation models make limited or no use of affec- tive channels in dialogue act classification. This paper presents a novel affect-enriched dialogue act …...

We evaluate several popular models of local discourse coherence for domain and task gen- erality by applying them to chat disentangle- ment. Using experiments on synthetic multi- party conversations, we show that most mod- els transfer well from text to dialogue. Co- herence models improve results overall when good parses …...

This paper focuses on mining the hypon- ymy (or is-a) relation from large-scale, open-domain web documents. A nonlinear probabilistic model is exploited to model the correlation between sentences in the aggregation of pattern matching results. Based on the model, we design a set of ev- idence combination and propagation algo- …...

This paper presents a supervised pronoun anaphora resolution system based on factorial hidden Markov models (FHMMs). The ba- sic idea is that the hidden states of FHMMs are an explicit short-term memory with an an- tecedent buffer containing recently described referents. Thus an observed pronoun can find its antecedent from …...

In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in discov- ering facts about entities and augmenting a knowledge base (KB) with these facts. This is done through two tasks, …...

Event extraction is the task of detecting certain specified types of events that are mentioned in the source language data. The state-of-the-art research on the task is transductive inference (e.g. cross-event inference). In this paper, we propose a new method of event extraction by well using cross-entity inference. In contrast …...

In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acqui- sition bottleneck associated with supervised role labelers. We present an algorithm that it- eratively splits and merges clusters represent- ing semantic roles, thereby leading from an initial clustering to a …...

In this work, we present a novel approach to the generation task of ordering prenomi- nal modifiers. We take a maximum entropy reranking approach to the problem which ad- mits arbitrary features on a permutation of modifiers, exploiting hundreds of thousands of features in total. We compare our error rates …...

We propose an automatic method of extracting paraphrases from definition sentences, which are also automatically acquired from the Web. We observe that a huge number of concepts are defined in Web documents, and that the sentences that define the same concept tend to convey mostly the same information using different …...

We consider a new subproblem of unsuper- vised parsing from raw text, unsupervised par- tial parsing—the unsupervised version of text chunking. We show that addressing this task directly, using probabilistic finite-state meth- ods, produces better results than relying on the local predictions of a current best unsu- pervised parser, Seginer’s …...

When designing grammars of natural lan- guage, typically, more than one formal anal- ysis can account for a given phenomenon. Moreover, because analyses interact, the choices made by the engineer influence the possibilities available in further grammar de- velopment. The order in which phenomena are treated may therefore have a …...

The goal of our research is to improve event extraction by learning to identify sec- ondary role filler contexts in the absence of event keywords. We propose a multi- layered event extraction architecture that pro- gressively “zooms in” on relevant informa- tion. Our extraction model includes a docu- ment genre …...

A system making optimal use of available in- formation in incremental language compre- hension might be expected to use linguistic knowledge together with current input to re- vise beliefs about previous input. Under some circumstances, such an error-correction capa- bility might induce comprehenders to adopt grammatical analyses that are inconsistent …...

We present a novel machine translation model which models translation by a linear sequence of operations. In contrast to the “N-gram” model, this sequence includes not only trans- lation but also reordering operations. Key ideas of our model are (i) a new reordering approach which better restricts the position to …...

This paper proposes a novel reordering model for statistical machine translation (SMT) by means of modeling the translation orders of the source language collocations. The model is learned from a word-aligned bilingual cor- pus where the collocated words in source sen- tences are automatically detected. During decoding, the model is …...

We present a novel computational formula- tion of speaker authority in discourse. This notion, which focuses on how speakers posi- tion themselves relative to each other in dis- course, is first developed into a reliable cod- ing scheme (0.71 agreement between human annotators). We also provide a computational model for …...

One of the major challenges facing statistical machine translation is how to model differ- ences in word order between languages. Al- though a great deal of research has focussed on this problem, progress is hampered by the lack of reliable metrics. Most current metrics are based on matching lexical items …...

This paper addresses a data-driven surface realisation model based on a large-scale re- versible grammar of German. We investigate the relationship between the surface realisa- tion performance and the character of the in- put to generation, i.e. its degree of underspec- ification. We extend a syntactic surface reali- sation system, …...

Argumentation schemes are structures or tem- plates for various kinds of arguments. Given the text of an argument with premises and con- clusion identified, we classify it as an instance of one of five common schemes, using features specific to each scheme. We achieve accura- cies of 63–91% in one-against-others …...

We present a novel model to represent and assess the discourse coherence of text. Our model assumes that coherent text implicitly favors certain types of discourse relation tran- sitions. We implement this model and apply it towards the text ordering ranking task, which aims to discern an original text from …...