The automated analysis of natural language data has become a central issue in the design of intelligent information systems. Processing unconstrained natural language data is still considered as an AI-hard task. However, various analysis techniques have been proposed to address specific aspects of natural language. In particular, recent interest has been focused on providing approximate analysis techniques, assuming that when perfect analysis is not possible, partial results may be still very useful.

Robustness has been traditionally stressed as a general desirable property of any computational model and system. The human NL interpretation device exhibits this property as the ability to deal with odd sentences. However, the difficulties in a theoretical explanation of robustness within the linguistic modelling suggested the adoption of an empirical notion. In this paper, we propose an empirical definition of robustness based on the notion of performance. Furthermore, a framework for controlling the parser robustness in the design phase is presented. The control is achieved via the adoption of two principles: the modularisation, typical of the software engineering practice, and the availability of domain adaptable components. The methodology has been adopted for the production of CHAOS, a pool of syntactic modules, which has been used in real applications. This pool of modules enables a large validation of the notion of empirical robustness, on the one side, and of the design methodology, on the other side, over different corpora and two different languages (English and Italian).

Robustness is a key issue for natural language processing in general and parsing in particular, and many approaches have been explored in the last decade for the design of robust parsing systems. Among those approaches is shallow or partial parsing, which produces minimal and incomplete syntactic structures, often in an incremental way. We argue that with a systematic incremental methodology one can go beyond shallow parsing to deeper language analysis, while preserving robustness. We describe a generic system based on such a methodology and designed for building robust analyzers that tackle deeper linguistic phenomena than those traditionally handled by the now widespread shallow parsers. The rule formalism allows the recognition of n-ary linguistic relations between words or constituents on the basis of global or local structural, topological and/or lexical conditions. It offers the advantage of accepting various types of inputs, ranging from raw to chunked or constituent-marked texts, so for instance it can be used to process existing annotated corpora, or to perform a deeper analysis on the output of an existing shallow parser. It has been successfully used to build a deep functional dependency parser, as well as for the task of co-reference resolution, in a modular way.

This paper proposes a robust approach to parsing suitable for Information Extraction (IE) from texts using finite-state cascades. The approach is characterized by the construction of an approximation of the full parse tree that captures all the information relevant for IE purposes, leaving the other relations underspecified. Sequences of cascades of finite-state rules deterministically analyze the text, building unambiguous structures. Initially basic chunks are analyzed; then clauses are recognized and nested; finally modifier attachment is performed and the global parse tree is built. The parsing approach allows robust, effective and efficient analysis of real world texts. The grammar organization simplifies changes, insertion of new rules and integration of domain-oriented rules. The approach has been tested for Italian, English, and Russian. A parser based on such an approach has been implemented as part of Pinocchio, an environment for developing and running IE applications.

The growing availability of textual sources has lead to an increase in the use of automatic knowledge acquisition approaches from textual data, as in Information Extraction (IE). Most IE systems use knowledge explicitly represented as sets of IE rules usually manually acquired. Recently, however, the acquisition of this knowledge has been faced by applying a huge variety of Machine Learning (ML) techniques. Within this framework, new problems arise in relation to the way of selecting and annotating positive examples, and sometimes negative ones, in supervised approaches, or the way of organizing unsupervised or semi-supervised approaches. This paper presents a new IE-rule learning system that deals with these training set problems and describes a set of experiments for testing this capability of the new learning approach.

This paper explores the effectiveness of index terms more complex than the single words used in conventional information retrieval systems. Retrieval is done in two phases: in the first, a conventional retrieval method (the Okapi system) is used; in the second, complex index terms such as syntactic relations and single words with part-of-speech information are introduced to rerank the results of the first phase. We evaluated the effectiveness of the different types of index terms through experiments using the TREC-7 test collection and 50 queries. The retrieval effectiveness was improved for 32 out of 50 queries. Based on this investigation, we then introduce a method to select effective index terms by using a decision tree. Further experiments with the same test collection showed that retrieval effectiveness was improved in 25 of the 50 queries.

Topic analysis is important for many applications dealing with texts, such as text summarization or information extraction. However, it can be done with great precision only if it relies on structured knowledge, which is difficult to produce on a large scale. In this paper, we propose using bootstrapping to solve this problem: a first topic analysis based on a weakly structured source of knowledge, a collocation network, is used for learning explicit topic representations that then support a more precise and reliable topic analysis.

This paper describes a simple discourse parsing and analysis algorithm that combines a formal underspecification utilising discourse grammar with Information Retrieval (IR) techniques. First, linguistic knowledge based on discourse markers is used to constrain a totally underspecified discourse representation. Then, the remaining underspecification is further specified by the computation of a topicality score for every discourse unit. This computation is done via the vector space model. Finally, the sentences in a prominent position (e.g. the first sentence of a paragraph) are given an adjusted topicality score. The proposed algorithm was evaluated by applying it to a text summarisation task. Results from a psycholinguistic experiment, indicating the most salient sentences for a given text as the ‘gold standard’, show that the algorithm performs better than commonly used machine learning and statistical approaches to summarisation.

We discuss robustness in LE systems from the perspective of engineering, and the predictability of both outputs and construction process that this entails. We present an architectural system that contributes to engineering robustness and low-overhead systems development (GATE, a General Architecture for Text Engineering). To verify our ideas we present results from the development of a multi-purpose cross-genre Named Entity recognition system. This system aims be robust across diverse input types, and to reduce the need for costly and timeconsuming adaptation of systems to new applications, with its capability to process texts from widely differing domains and genres.