Articles

We describe an implementation integrat- ing a spoken dialogue system with a mo- bile robot, which the user can direct to specific locations, ask for information about its status, and supply information about its environment. The robot uses an internal map for navigation, and com- municates its current orientation and …...

We describe how unknown lexical en- tries are processed in a unification-based framework with large-coverage gram- mars and how from their usage lexi- cal entries are extracted. To keep the time and space usage during parsing within bounds, information from exter- nal sources like Part of Speech (PoS) taggers and …...

We develop a general feature space for automatic classification of verbs into lexical semantic classes. Previous work was limited in scope by the need for manual selection of discriminating fea- tures, through a linguistic analysis of the target verb classes (Merlo and Steven- son, 2001). We instead analyze the classification …...

This paper presents a formalization of automatic grammar acquisition that is based on lexicalized grammar for- malisms (e.g. LTAG and HPSG). We state the conditions for the consistent ac- quisition of a unique lexicalized gram- mar from an annotated corpus. 1...

This work studies Named Entity Recog- nition (NER) for Catalan without mak- ing use of annotated resources of this language. The approach presented is based on machine learning techniques and exploits Spanish resources, either by first training models for Spanish and then translating them into Catalan, or by directly training …...

The choice of verb features is crucial for the learning of verb classes. This pa- per presents clustering experiments on 168 German verbs, which explore the relevance of features on three levels of verb description, purely syntactic frame types, prepositional phrase information and selectional preferences. In contrast to previous approaches …...

The paper shows how Combinatory Cat- egorial Grammar (CCG) can be adapted to take advantage of the extra resource- sensitivity provided by the Categorial Type Logic framework. The resulting reformulation, Multi-Modal CCG, sup- ports lexically specified control over the applicability of combinatory rules, per- mitting a universal rule component and …...

We develop a new approach to learn- ing phrase translations from parallel cor- pora, and show that it performs with very high coverage and accuracy in choosing French translations of English named-entity phrases in a test corpus of software manuals. Analysis of a subset of our results suggests that the …...

Previous work on the induction of se- lectional preferences has been mainly carried out for English and has concen- trated almost exclusively on verbs and their direct objects. In this paper, we focus on class-based models of selec- tional preferences for German verbs and take into account not only direct …...

In (Kanazawa, 1998) it was shown that rigid Classical Categorial Gram- mars are learnable (in the sense of (Gold, 1967)) from strings. Surpris- ingly there are recent negative results for, among others, rigid associative Lamb ek (L) grammars. In this paper the non-lcarnability of the class of rigid grammars in …...

We propose a semantic construction me- thod for Feature-Based Tree Adjoining Grammar which is based on the derived tree, compare it with related proposals and briefly discuss some implementation possibilities. 1...

Syntactic constraints in Koskenniemi's Finite-State Intersection Grammar (FSIG) are logically less complex than their formalism (Koskenniemi et al., 1992) would suggest: It turns out that although the constraints in Voutilainen's (1994) FSIG description of English make use of several extensions to regular expressions, the description as a whole reduces to …...

The goal of interactive machine transla- tion is to improve the productivity of hu- man translators. An interactive machine translation system operates as follows: the automatic system proposes a transla- tion. Now, the human user has two op- tions: to accept the suggestion or to cor- rect it. During the …...

In this paper, experiments on automatic extraction of keywords from abstracts us- ing a supervised machine learning algo- rithm are discussed. The main point of this paper is that by adding linguistic know- ledge to the representation (such as syn- tactic features), rather than relying only on statistics (such as …...

We explore how virtual examples (artifi- cially created examples) improve perfor- mance of text classification with Support Vector Machines (SVMs). We propose techniques to create virtual examples for text classification based on the assump- tion that the category of a document is un- changed even if a small number of …...

When building a Chinese named entity recognition system, one must deal with certain language-specific issues such as whether the model should be based on characters or words. While there is no unique answer to this question, we discuss in detail advantages and disadvantages of each model, identify problems in segmen- …...

We used the memory-based learner Timbl (Daelemans et al., 2002) to find names in En- glish and German newspaper text. A first sys- tem used only the training data, and a number of gazetteers. The results show that gazetteers are not beneficial in the English case, while they are for …...

This paper describes a robust linear classifica- tion system for Named Entity Recognition. A similar system has been applied to the CoNLL text chunking shared task with state of the art performance. By using different linguistic fea- tures, we can easily adapt this system to other token-based linguistic tagging problems. …...

We present a named entity recognition and classification system that uses only probabilis- tic character-level features. Classifications by multiple orthographic tries are combined in a hidden Markov model framework to incorpo- rate both internal and contextual evidence. As part of the system, we perform a preprocess- ing stage in which …...

This paper investigates stacking and voting methods for combining strong classifiers like boosting, SVM, and TBL, on the named-entity recognition task. We demonstrate several ef- fective approaches, culminating in a model that achieves error rate reductions on the develop- ment and test sets of 63.6% and 55.0% (En- glish) and …...