Intelligent indexing of crime scene photographs

The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently

Beyond definition: Organising semantic information in bilingual dictionaries

This paper considers the process of organising semantic information in bilingual dictionaries with diachronic coverage, from selecting the textual source-material to designing the entries. The discussion centres on practical aspects of ancient Greek lexicography. First, the traditional semantic frameworks are described. Then, more recent approaches are noted, notably those of Adrados and of Chadwick, both of which aim to integrate contextual data within a semantic framework. Since the relevance of contextual information varies with lemma part of speech, different configurations are required for entries describing nouns, adjectives, and verbs. These are illustrated by three entries from a Greek-English dictionary currently being written at Cambridge. In order to organise data to this level of specificity, stylistic templates are indispensable, and digital software provides a means of providing them. However, systems designed for writing new dictionaries require different features from those designed for encoding pre-existing texts. A description is given of how the lexicographic requirements of the Cambridge dictionary were met by a user-designed system

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Floating constraints in lexical choice

Lexical choice is a computationally complex task, requiring a generation system to consider a potentially large number of mappings between concepts and words. Constraints that aid in determining which word is best come from a wide variety of sources, including syntax, semantics, pragmatics, the lexicon, and the underlying domain. Furthermore, in some situations, different constraints come into play early on, while in others, they apply much later. This makes it difficult to determine a systematic ordering in which to apply constraints. In this paper, we present a general approach to lexical choice that can handle multiple, interacting constraints. We focus on the problem of floating constraints, semantic or pragmatic constraints that float, appearing at a variety of different syntactic ranks, often merged with other semantic constraints. This means that multiple content units can be realized by a single surface element, and conversely, that a single content unit can be realized by a variety of surface elements. Our approach uses the Functional Unification Formalism (FUF) to represent a generation lexicon, allowing for declarative and compositional representation of individual constraints