Finite state methods have been in common use in various areas of natural language processing (NLP) for many years. A series of specialized workshops in this area illustrates this. In 1996, András Kornai organized a very successful workshop entitled Extended Finite State Models of Language. One of the results of that workshop was a special issue of Natural Language Engineering (Volume 2, Number 4). In 1998, Kemal Oflazer organized a workshop called Finite State Methods in Natural Language Processing. A selection of submissions for this workshop were later included in a special issue of Computational Linguistics (Volume 26, Number 1). Inspired by these events, Lauri Karttunen, Kimmo Koskenniemi and Gertjan van Noord took the initiative for a workshop on finite state methods in NLP in Helsinki, as part of the European Summer School in Language, Logic and Information. As a related special event, the 20th anniversary of two-level morphology was celebrated. The appreciation of these events led us to believe that once again it should be possible, with some additional submissions, to compose an interesting special issue of this journal.

We aim at finding the minimal set of fragments that achieves maximal parse accuracy in Data Oriented Parsing (DOP). Experiments with the Penn Wall Street Journal (WSJ) treebank show that counts of almost arbitrary fragments within parse trees are important, leading to improved parse accuracy over previous models tested on this treebank. We isolate a number of dependency relations which previous models neglect but which contribute to higher accuracy. We show that the history of statistical parsing models displays a tendency towards using more and larger fragments from training data.

This paper has two purposes. First, it suggests a formal approach for specifying and verifying lingware. This approach is based on a unified notation of the main existing formalisms for describing linguistic knowledge (i.e. Formal Grammars, Unification Grammars, HPSG, etc.) on the one hand, and the integration of data and processing on the other. Accordingly, a lingware specification includes all related aspects in a unified framework. This facilitates the development of a lingware system, since one has to follow a single development process instead of two separate ones. Secondly, it presents an environment for the formal specification of lingware, based on the suggested approach, which is neither restricted to a particular kind of application nor to a particular class of linguistic formalisms. This environment provides interfaces enabling the specification of both linguistic knowledge and functional aspects of a lingware system. Linguistic knowledge is specified with the usual grammatical formalisms, whereas functional aspects are specified with a suitable formal notation. Both descriptions will be integrated into the same framework to obtain a complete requirement specification that can be refined towards an executable program.

This paper starts by introducing a class of future document authoring systems that will allow authors to specify the content and form of a text+pictures document at a high level of abstraction, while leaving responsibility for linguistic and graphical details to the system. Next, we describe two working prototypes that implement parts of this functionality, based on semantic modeling of the pictures and the text of the document; one of these two, the ILLUSTRATE prototype, is a multimedia extension of previous text authoring systems in the What You See Is What You Meant (WYSIWYM) tradition. The paper concludes with an exploration of the ways in which Multimedia WYSIWYM can be further enhanced, allowing it to approximate the ‘ideal’ systems that were sketched earlier in the paper. Applications of Multimedia WYSIWYM to general-purpose picture retrieval (in the context of the Semantic Web, for example) are also discussed.

Textual question answering is a technique of extracting a sentence or text snippet from a document or document collection that responds directly to a query. Open-domain textual question answering presupposes that questions are natural and unrestricted with respect to topic. The question answering (Q/A) techniques, as embodied in today's systems, can be roughly divided into two types: (1) techniques for Information Seeking (IS), which localize the answer in vast document collections; and (2) techniques for Reading Comprehension (RC) that answer a series of questions related to a given document. Although these two types of techniques and systems are different, it is desirable to combine them for enabling more advanced forms of Q/A. This paper discusses an approach that successfully enhanced an existing IS system with RC capabilities. This enhancement is important because advanced Q/A, as exemplified by the ARDA AQUAINT program, is moving towards Q/A systems that incorporate semantic and pragmatic knowledge enabling dialogue-based Q/A. Because today's RC systems involve a short series of questions in context, they represent a rudimentary form of interactive Q/A which constitutes a possible foundation for more advanced forms of dialogue-based Q/A.

Given the lack of word delimiters in written Japanese, word segmentation is generally considered a crucial first step in processing Japanese texts. Typical Japanese segmentation algorithms rely either on a lexicon and syntactic analysis or on pre-segmented data; but these are labor-intensive, and the lexico-syntactic techniques are vulnerable to the unknown word problem. In contrast, we introduce a novel, more robust statistical method utilizing unsegmented training data. Despite its simplicity, the algorithm yields performance on long kanji sequences comparable to and sometimes surpassing that of state-of-the-art morphological analyzers over a variety of error metrics. The algorithm also outperforms another mostly-unsupervised statistical algorithm previously proposed for Chinese. Additionally, we present a two-level annotation scheme for Japanese to incorporate multiple segmentation granularities, and introduce two novel evaluation metrics, both based on the notion of a compatible bracket, that can account for multiple granularities simultaneously.

Most information systems that deal with natural language texts do not tolerate much deviation from their idealized and simplified model of language. Spoken dialog is notoriously ungrammatical, however. Because the MAREDI project focuses in particular on the automatic analysis of scripted dialogs, we needed to develop a robust capacity to analyze transcribed spoken language. This paper summarizes the current state of our work. It presents the main elements of our approach, which is based on exploiting surface markers as the best route to the semantics of the conversation modelled. We highlight the foundations of our particular conversational model, and give an overview of the MAREDI system. We then discuss its three key modules, a connectionist network to recognise speech acts, a robust syntactic analyzer, and a semantic analyzer.

Hyphenation is the task of identifying potential hyphenation points inwords. In this paper, three finite-state hyphenation methods for Dutch are presented and compared in terms of accuracy and size of the resulting automata.

This study aims to improve the performance of identifying grammatical functions between an adnoun clause and a noun phrase in Korean. The key task is to determine the relation between the two constituents in terms of such functional categories as subject, object, adverbial and appositive. The problem is mainly caused by the fact that functional morphemes, which are considered to be crucial for identifying the relation, are omitted in the noun phrases. To tackle this problem, we propose to employ the Support Vector Machines (SVM) in determining the grammatical functions. Through an experiment with a tagged corpus for training SVMs, we found the proposed model to be more useful than both the Maximum Entropy Model (MEM) and the backed-off model.

The main issue when building Information Extraction (IE) systems is how to obtain the knowledge needed to identify relevant information in a document. Most approaches require expert human intervention in many steps of the acquisition process. In this paper we describe ESSENCE, a new method for acquiring IE patterns that significantly reduces the need for human intervention. The method is based on ELA, a specifically designed learning algorithm for acquiring IE patterns without tagged examples. The distinctive features of ESSENCE and ELA are that (1) they permit the automatic acquisition of IE patterns from unrestricted and untagged text representative of the domain, due to (2) their ability to identify regularities around semantically relevant concept-words for the IE task by (3) using non-domain-specific lexical knowledge tools such as WordNet, and (4) restricting the human intervention to defining the task, and validating and typifying the set of IE patterns obtained. Since ESSENCE does not require a corpus annotated with the type of information to be extracted and it uses a general purpose ontology and widely applied syntactic tools, it reduces the expert effort required to build an IE system and therefore also reduces the effort of porting the method to any domain. The results of the application of ESSENCE to the acquisition of IE patterns in an MUC-like task are shown.

The GEIG metric for quantifying the accuracy of parsing became influential through the Parseval programme, but many researchers have seen it as unsatisfactory. The Leaf-Ancestor (LA) metric, first developed in the 1980s, arguably comes closer to formalizing our intuitive concept of relative parse accuracy. We support this claim via an experiment that contrasts the performance of alternative metrics on the same body of automatically-parsed examples. The LA metric has the further virtue of providing straightforward indications of the location of parsing errors.

The paper investigates the computational complexity of different versions of Optimality Theory (OT). The result of Frank and Satta (1998) is used as a starting point. These authors show that unidirectional optimization can be implemented by finite state techniques if only binary constraints are used. The consequences of (a) taking gradient constraints into account and (b) using bidirectional optimization in the sense of Blutner (2000) are explored. The central result of the paper is that the combination of gradient constraints and bidirectionality leads to a massive increase of computational complexity.

This paper presents the design and implementation of a finite-state syntactic grammar of Basque that has been used with the objective of extracting information about verb subcategorization instances from newspaper texts. After a partial parser has built basic syntactic units such as noun phrases, prepositional phrases, and sentential complements, a finite-state parser performs syntactic disambiguation, determination of clause boundaries and filtering of the results, in order to obtain a verb occurrence together with its associated syntactic components, either complements or adjuncts. The set of occurrences for each verb is then filtered by statistical measures that distinguish arguments from adjuncts.

In this paper, we describe a system for coreference resolution and emphasize the role of evaluation for its design. The goal of the system is to group referring expressions (identified beforehand in narrative texts) into sets of coreferring expressions that correspond to discourse entities. Several knowledge sources are distinguished, such as referential compatibility between a referring expression and a discourse entity, activation factors for discourse entities, size of working memory, or meta-rules for the creation of discourse entities. For each of them, the theoretical analysis of its relevance is compared to scores obtained through evaluation. After looping through all knowledge sources, an optimal behavior is chosen, then evaluated on test data. The paper also discusses evaluation measures as well as data annotation, and compares the present approach to others in the field.

We present an Augmented Template-Based approach to text realization that addresses the requirements of real-time, interactive systems such as a dialog system or an intelligent tutoring system. Template-based approaches are easier to implement and use than traditional approaches to text realization. They can also generate texts more quickly. However traditional template-based approaches with rigid templates are inflexible and difficult to reuse. Our approach augments traditional template-based approaches by adding several types of declarative control expressions and an attribute grammar-based mechanism for processing missing or inconsistent slot fillers. Therefore, augmented templates can be made more general than traditional ones, yielding templates that are more flexible and reusable across applications.

This paper presents the results of a study on information extraction from unrestricted Turkish text using statistical language processing methods. In languages like English, there is a very small number of possible word forms with a given root word. However, languages like Turkish have very productive agglutinative morphology. Thus, it is an issue to build statistical models for specific tasks using the surface forms of the words, mainly because of the data sparseness problem. In order to alleviate this problem, we used additional syntactic information, i.e. the morphological structure of the words. We have successfully applied statistical methods using both the lexical and morphological information to sentence segmentation, topic segmentation, and name tagging tasks. For sentence segmentation, we have modeled the final inflectional groups of the words and combined it with the lexical model, and decreased the error rate to 4.34%, which is 21% better than the result obtained using only the surface forms of the words. For topic segmentation, stems of the words (especially nouns) have been found to be more effective than using the surface forms of the words and we have achieved 10.90% segmentation error rate on our test set according to the weighted TDT-2 segmentation cost metric. This is 32% better than the word-based baseline model. For name tagging, we used four different information sources to model names. Our first information source is based on the surface forms of the words. Then we combined the contextual cues with the lexical model, and obtained some improvement. After this, we modeled the morphological analyses of the words, and finally we modeled the tag sequence, and reached an F-Measure of 91.56%, according to the MUC evaluation criteria. Our results are important in the sense that, using linguistic information, i.e. morphological analyses of the words, and a corpus large enough to train a statistical model significantly improves these basic information extraction tasks for Turkish.

In this paper, we present a new Deterministic Finite Automata (DFA) minimization algorithm. The algorithm is incremental – it may be halted at any time, yielding a partially-minimized automaton. All of the other (known) minimization algorithms have intermediate results which are not useable for partial minimization. Since the first algorithm is easily understood but inefficient, we consider three practical and effective optimizations. The first two optimizations do not affect the asymptotic worst-case running time – though they perform well on a large class of automata. The third optimization yields an quadratic-time algorithm which is competitive with the previously known ones.

A principal aim of this journal is to bridge the gap between traditional computational linguistics research and the implementation of practical applications with potential real-world use. This new column aims to help with that bridge-building, by providing a window on to what is happening with speech and language technologies in the industrial and commercial worlds, and a discussion forum for topics of interest in the technology transfer space.

This paper describes how the use of monadic second-order logic for specifying regular languages can be extended for specifying regular relations, providing a declarative description language for finite state transductions of the sort used in NLP. We discuss issues arising in the integration into an automaton toolkit of an implementation of the conversion from logic formulas to automata. The utility of the logic of regular relations is demonstrated by showing how it can be used to define the family of replacement operators in a way that lends itself to straightforward proofs of correctness.