Articles

Corpus-based grz.mmar induction relies on us- ing many hand-parsed sentences as training examples. However, the construction of a training corpus with detailed syntactic analy- sis for every sentence is a labor-intensive task. We propose to use sample selection methods to minimize the amount of annotation needed in the training data, …...

This paper presents a method of Japanese dependency structure analysis based on Sup-- port Vector Machines (SVMs). Conventional parsing techniques based on Machine Learn- ing framework, such as Decision Trees and Maximum Entropy Models, have difficulty in selecting useful features as well as find- ing appropriate combination of selected fea- …...

We believe that identifying the structure of scien- tific argumentation in articles can help in tasks such as automatic summarization or the auto- mated construction of citation indexes. One par- ticularly important aspect of this structure is the question of who a given scientific statement is at- tributed to: other …...

A wide range of natural language problems can be viewed as disambiguating between a small set of alternatives based upon the string context surrounding the ambiguity site. In this paper we demonstrate that classification accuracy can be improved by invoking a more descriptive feature set than what is typically used. …...

The purpose of this work is to investigate the process of grammatical acquisition from data. We are using a computational learning sys- tern that is composed of a Universal Grammar with associated parameters, and a learning al- gorithm, following the Principles and Parame- ters Theory. The Universal Grammar is imple- …...

In the context of language learning, we address a logical approach to information extraction. The system INTHELEX, used to carry out this task, requires a logic representation of sentences to run the learning algorithm. Hence, the need for parsers to produce structured representa- tions from raw text. This led us …...

In this paper, we propose an Inductive Logic Programming learning method which aims at automatically extracting special Noun-Verb (N- V) pairs from a corpus in order to build up semantic lexicons based on Pustejovsky's Gen- erative Lexicon (GL) principles (Pustejovsky, 1995). In one of the components of this lex- ical …...

We report work on effectively incorporating lin- guistic knowledge into grammar induction. We use a highly interactive bottom-up inductive logic programming (ILP) algorithm to learn 'missing' grammar rules from an :incomplete grammar. Using linguistic constraints on, for example, head features and gap threading, re- duces the search space to such …...

In this paper we study learning from a logical perspective. We show that there is a strong re- lationship between a learning strategy, its for- mal learning framework and its logical represen- tational theory. This relationship enables one to translate learnability results from one theory to another. Moreover if we …...

This paper describes our actual and ongoing work in supporting semi-automatic ontology ac- quisition from a corporate intranet of an in- surance company. A comprehensive architec- ture and a system for semi-automatic ontology acquisition supports processing semi-structured information (e.g. contained in dictionaries) and natural language documents and includ- ing existing …...

This paper proposes an error-driven HMM- based text chunk tagger with context-dependent lexicon. Compared with standard HMM-based tagger, this tagger incorporates more contextual information into a lexical entry. Moreover, an error-driven learning approach is adopted to de- crease the memory requirement by keeping only positive lexical entries and makes it …...

Treating shallow parsing as part-of-speech tag- ging yields results comparable with other, more elaborate approaches. Using the CoNLL 2000 training and testing material, our best model had an accuracy of 94.88%, with an overall FB1 score of 91.94%. The individual FB1 scores for NPs were 92.19%, VPs 92.70% and PPs …...

In this paper, we compare three different ap- proaches to build a probabilistic context-free grammar for natural language parsing from a tree bank corpus: 1) a model that simply ex- tracts the rules contained in the corpus and counts the number of occurrences of each rule 2) a model that …...

Spam filtering is a text categorization task that shows especial features that make it interest- ing and difficult. First, the task has been per- formed traditionally using heuristics from the domain. Second, a cost model is required to avoid misclassification of legitimate messages. We present a comparative evaluation of several …...

In this paper are described experiments on un- supervised learning of the domain lexicon and relevant phrase fragments from a dialog cor- pus. Suggested approach is based on using do- main independent words for chunking and us- ing semantical predictional power of such words for clustering and automatic extraction phrase …...

In this paper we describe the construction of a part-of-speech tagger both for medical doc- ument retrieval purposes and XP extraction. Therefore we have designed a double system: for retrieval purposes, we rely on a rule-based ar- chitecture, called minimal commitment, which is likely to be completed by a data-driven …...

This paper addresses the issue of the automatic induction of syntactic categories from unanno- tared corpora. Previous techniques give good results, but fail to cope well with ambiguity or rare words. An algorithm, context distribution clustering (CDC), is presented which can be naturally extended to handle these problems. 1...

We present ongoing work on prosody predic- tion for speech synthesis. This approach con- siders sentences as tree structures and infers the prosody from a corpus of such structures using machine learning techniques. The predic- tion is achieved from the prosody of the closest sentence of the corpus through tree …...

The Maximum Entropy principle (ME) is an ap- propriate framework for combining information of a diverse nature from several sources into the same language model. In order to incorporate long-distance information into the ME frame- work in a language model, a Whole Sentence Maximum Entropy Language Model (WSME) could be …...

Morphology induction is a subproblem of important tasks like automatic learning of machine-readable dictionaries and grammar in- duction. Previous morphology induction ap- proaches have relied solely on statistics of hy- pothesized stems and affixes to choose which affixes to consider legitimate. Relying on stem- and-affix statistics rather than semantic knowl- …...