Articles

tra Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to per- form domain adaptation for statistical machine translation, where in-domain bi- lingual corpora do not exist. This method first uses out-of-domain corpora to train a …...

It is difficult to identify sentence impor- tance from a single point of view. In this paper, we propose a learning-based ap- proach to combine various sentence fea- tures. They are categorized as surface, content, relevance and event features. Surface features are related to extrinsic aspects of a sentence. Content …...

Previous methods usually conduct the keyphrase extraction task for single docu- ments separately without interactions for each document, under the assumption that the documents are considered inde- pendent of each other. This paper pro- poses a novel approach named Col- labRank to collaborative single-document keyphrase extraction by making use of …...

We present recent work in the area of Cross-Domain Dialogue Act tagging. Our experiments investigate the use of a sim- ple dialogue act classifier based on purely intra-utterance features - principally in- volving word n-gram cue phrases. We ap- ply automatically extracted cues from one corpus to a new annotated …...

Various types of structural information - e.g., about the type of constructions in which binding constraints apply, or about the structure of names - play a central role in coreference resolution, often in combi- nation with lexical information (as in ex- pletive detection). Kernel functions ap- pear to be a …...

In this paper, we extend an existing para- graph retrieval approach to why-question answering. The starting-point is a system that retrieves a relevant answer for 73% of the test questions. However, in 41% of these cases, the highest ranked relevant answer is not ranked in the top-10. We aim to …...

Creative metaphor is a phenomenon that stretches and bends the conventions of se- mantic description, often to humorous and poetic extremes. The computational mod- eling of metaphor thus requires a knowl- edge representation that is just as stretch- able and semantically accommodating. We present here a flexible knowledge repre- sentation …...

This paper shows that it is very often possible to identify the source language of medium-length speeches in the EU- ROPARL corpus on the basis of fre- quency counts of word n-grams (87.2%- 96.7% accuracy depending on classifica- tion method). The paper also examines in detail which positive markers are …...

We report on the large-scale acquisition of class attributes with and without the use of lists of representative instances, as well as the discovery of unary attributes, such as typically expressed in English through prenominal adjectival modifica- tion. Our method employs a system based on compositional language processing, as applied …...

This paper presents an approach for sub- stantially reducing the time needed to cal- culate the shortest paths between all con- cepts in a wordnet. The algorithm exploits the unique “star-like” topology of word- nets to cut down on time-expensive calcu- lations performed by algorithms to solve the all-pairs shortest …...

Words in Chinese text are not naturally separated by delimiters, which poses a challenge to standard machine translation (MT) systems. In MT, the widely used approach is to apply a Chinese word seg- menter trained from manually annotated data, using a fixed lexicon. Such word segmentation is not necessarily optimal …...

Recognizing analogies, synonyms, anto- nyms, and associations appear to be four distinct tasks, requiring distinct NLP al- gorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four seman- tic classes, however, are a tiny sample of the full range of semantic …...

We address corpus building situations, where complete annotations to the whole corpus is time consuming and unrealistic. Thus, annotation is done only on crucial part of sentences, or contains unresolved label ambiguities. We propose a parame- ter estimation method for Conditional Ran- dom Fields (CRFs), which enables us to use …...

State-of-the-art statistical parsing models applied to free word-order languages tend to underperform compared to, e.g., pars- ing English. Constituency-based mod- els often fail to capture generalizations that cannot be stated in structural terms, and dependency-based models employ a ‘single-head’ assumption that often breaks in the face of multiple exponence. In …...

In this paper, we propose a data-oriented method for inferring the emotion of a speaker conversing with a dialog system from the semantic content of an utterance. We first fully automatically obtain a huge collection of emotion-provoking event in- stances from the Web. With Japanese cho- sen as a target …...

The task of identifying redundant infor- mation in documents that are generated from multiple sources provides a signifi- cant challenge for summarization and QA systems. Traditional clustering techniques detect redundancy at the sentential level and do not guarantee the preservation of all information within the document. We discuss an algorithm …...

Few attempts have been made to inves- tigate the utility of temporal reasoning within machine learning frameworks for temporal relation classification between events in news articles. This paper presents three settings where temporal reasoning aids machine learned classifiers of tempo- ral relations: (1) expansion of the dataset used for learning; …...

In this paper, an extension of a dimen- sionality reduction algorithm called NON- NEGATIVE MATRIX FACTORIZATION is presented that combines both ‘bag of words’ data and syntactic data, in order to find semantic dimensions according to which both words and syntactic relations can be classified. The use of three way …...

In this paper we describe a methodology for detecting preposition errors in the writ- ing of non-native English speakers. Our system performs at 84% precision and close to 19% recall on a large set of stu- dent essays. In addition, we address the problem of annotation and evaluation in this …...

Shallow parsing is one of many NLP tasks that can be reduced to a sequence la- beling problem. In this paper we show that the latent-dynamics (i.e., hidden sub- structure of shallow phrases) constitutes a problem in shallow parsing, and we show that modeling this intermediate structure is useful. By …...