Articles

In statistical machine translation, the gen- eration of a translation hypothesis is com- putationally expensive. If arbitrary word- reorderings are permitted, the search prob- lem is NP-hard. On the other hand, if we restrict the possible word-reorderings in an appropriate way, we obtain a polynomial-time search algorithm. In this paper, …...

Traditional vector-based models use word co-occurrence counts from large corpora to represent lexical meaning. In this pa- per we present a novel approach for con- structing semantic spaces that takes syn- tactic relations into account. We introduce a formalisation for this class of models and evaluate their adequacy on two …...

We introduce a MetaGrammar, which al- lows us to automatically generate, from a single and compact MetaGrammar hier- archy, parallel Lexical Functional Gram- mars (LFG) and Tree-Adjoining Gram- mars (TAG) for French and for English: the grammar writer specifies in compact manner syntactic properties that are po- tentially framework-, and …...

The paper describes two parsing schemes: a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar. It dis- cusses several ways to combine them and presents evaluation results for the two in- dividual approaches and their combina- tion. An underspecification scheme for the output …...

We present a novel, data-driven method for integrated shallow and deep parsing. Mediated by an XML-based multi-layer annotation architecture, we interleave a robust, but accurate stochastic topological field parser of German with a constraint- based HPSG parser. Our annotation-based method for dovetailing shallow and deep phrasal constraints is highly flexible, …...

We present a probabilistic parsing model for German trained on the Negra tree- bank. We observe that existing lexicalized parsing models using head-head depen- dencies, while successful for English, fail to outperform an unlexicalized baseline model for German. Learning curves show that this effect is not due to lack of …...

We augment a model of translation based on re-ordering nodes in syntactic trees in order to allow alignments not conforming to the original tree structure, while keep- ing computational complexity polynomial in the sentence length. This is done by adding a new subtree cloning operation to either tree-to-string or tree-to-tree …...

Word alignment plays a crucial role in sta- tistical machine translation. Word-aligned corpora have been found to be an excellent source of translation-related knowledge. We present a statistical model for comput- ing the probability of an alignment given a sentence pair. This model allows easy in- tegration of context-specific features. …...

Previous research has demonstrated the utility of clustering in inducing semantic verb classes from undisambiguated cor- pus data. We describe a new approach which involves clustering subcategoriza- tion frame (SCF) distributions using the Information Bottleneck and nearest neigh- bour methods. In contrast to previous work, we particularly focus on cluster- …...

Automatically acquiring synonymous col- location pairs such as <turn on, OBJ, light> and <switch on, OBJ, light> from corpora is a challenging task. For this task, we can, in general, have a large monolingual corpus and/or a very limited bilingual corpus. Methods that use monolingual corpora alone or use bilingual …...

We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparse- ness. We partially overcome this problem by integrating a thesaurus and introduc- …...

Kernel-based learning (e.g., Support Vec- tor Machines) has been successfully ap- plied to many hard problems in Natural Language Processing (NLP). In NLP, al- though feature combinations are crucial to improving performance, they are heuris- tically selected. Kernel methods change this situation. The merit of the kernel methods is that …...

We have aligned Japanese and English news articles and sentences to make a large parallel corpus. We first used a method based on cross-language informa- tion retrieval (CLIR) to align the Japanese and English articles and then used a method based on dynamic programming (DP) matching to align the Japanese …...

We introduce a probabilistic noisy- channel model for question answering and we show how it can be exploited in the context of an end-to-end QA system. Our noisy-channel system outperforms a state- of-the-art rule-based QA system that uses similar resources. We also show that the model we propose is flexible …...

Recent work in Question Answering has focused on web-based systems that extract answers using simple lexico- syntactic patterns. We present an alternative strategy in which patterns are used to extract highly precise relational information offline, creating a data repository that is used to efficiently answer questions. We evaluate our strategy …...

This paper proposes the “Hierarchical Di- rected Acyclic Graph (HDAG) Kernel” for structured natural language data. The HDAG Kernel directly accepts several lev- els of both chunks and their relations, and then efficiently computes the weighed sum of the number of common attribute sequences of the HDAGs. We applied the …...

In this paper we present a novel, cus- tomizable IE paradigm that takes advan- tage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and (2) inductive …...

We present a domain-independent topic segmentation algorithm for multi-party speech. Our feature-based algorithm com- bines knowledge about content using a text-based algorithm as a feature and about form using linguistic and acous- tic cues about topic shifts extracted from speech. This segmentation algorithm uses automatically induced decision rules to combine …...

We investigate the verbal and nonverbal means for grounding, and propose a design for embodied conversational agents that re- lies on both kinds of signals to establish common ground in human-computer inter- action. We analyzed eye gaze, head nods and attentional focus in the context of a di- rection-giving task. …...

We describe the ongoing construction of a large, semantically annotated corpus resource as reliable basis for the large- scale acquisition of word-semantic infor- mation, e.g. the construction of domain- independent lexica. The backbone of the annotation are semantic roles in the frame semantics paradigm. We report expe- riences and evaluate …...