for presentation at the AAAI Symposium on Cross-Language Text and Speech Retrieval, Stanford, 1997
Language Technology Lab
1. Overview and Objectives
2.1 Application Domains3. Technologies
2.2 Cross-Language Text Retrieval2.2.1 Translation of Documents2.3 Concept-based retrieval
2.2.2 Translation of Index Terms and Queries
2.2.3 Relevance Feedback with Parallel Texts
2.2.4 Machine Translation for Relevance Feedback2.3.1 Disambiguation and Domain Modelling2.4 Extraction of terminology from multilingual corpora
2.3.2 Grammatical Relations and Phrasal Indexing
2.5 Navigation tools2.5.1 Interactive Search2.6 Multilingual Document and Website Management
2.5.2 Filtering Options
3.1 Information Retrieval Engine4. User-driven approach
3.2 Language Identification
3.3 Machine Translation
3.4 Morphological Analysis and Part-of-Speech Tagging
3.6 Document Classification
3.7 Shallow Parsing and Information Extraction
3.8 Multilingual Document and Website Management
4.1 Who are the Users?5. Evaluation
4.2 User Interviews
4.3 User Monitoring
4.4 User Evaluation of Prototypes
6. The Consortium
Leading-edge application projects aim at advanced applications based on existing or emerging IC components and novel Language Engineering technologies. The goal is to meet user requirements dictated by socio-economic changes over the next few years. (from the call for project proposals for the Telematics Application Programme).
The socio-economic changes addressed by the MULINEX project are the emergence and widespread acceptance of the WWW, the increasing availability of gigabytes of information in different languages, and the increasing number of people with different mother tongues who need to find information on the web.
Providers of web search engines are already producing localised versions for different countries (e.g., lycos.de for Germany), but so far these provide only the user interface and the advertisements in the local language, but the search and retrieval process itself is not language-aware.
The technologies to be used in the project include a state-of-the-art information retrieval system, advanced linguistic processing tools (morphological analysis, information extraction, lexical semantics), algorithms for alignment of translated texts and terminology extraction, and machine translation systems.
The intended prototype application can run entirely on the server of a content provider or search service operator, so that the end user needs only a standard web browser such as Netscape Navigator, Alis Tango or Microsoft Explorer. The project is committed to supporting open web standards and will avoid dependence on proprietary formats and solutions, in order to make the results applicable to a wider user base.
The application will be realised as a group of interacting tools which improve access to information (search and navigation) in multilingual web document collections, and support the creation and maintenance of multilingual content for the web by information providers. The set of tools will provide the following search, retrieval and navigation functionality for the end user:
|1.||search by a combination of keywords, phrases, and concepts|
|2.||retrieval of documents in different languages with one monolingual query through multilingual indexing|
|3.||online generation and presentation of navigation maps or menus for supporting interactive refinement of query and search|
|4.||exploitation of context and user profiling information for selecting relevant documents|
In addition, it will offer functionalities for the management of multilingual websites. These will only be discussed in this paper insofar as they are relevant to cross-language text retrieval.
In the project MULINEX, we consider retrieval and navigation tools for two different kinds of application domains related to the WWW: On the one hand, the project investigates how to provide and improve cross-language retrieval performance for unrestricted search engines such as HotBot, Lycos, or AltaVista that index the entire content of the WWW (or thematically unrestricted portions of it). On the other hand, we consider search services which provide for information retrieval services for single (thematically restricted) web sites. An open, thematically unrestricted, application domain imposes different requirements than a narrow, thematically restricted, one.
In an open domain, it is necessary to automatically identify the language of a document. It will be useful to perform an automatic thematic classification of documents in order to present additional information about retrieved documents to the user. Cross-language text retrieval for an open domain will have to rely on general-purpose translation dictionaries and language technologies.
For a restricted domain, on the other hand, it is possible to use domain-specific dictionaries, thesauri, and language technologies in order to improve retrieval performance. If translated documents are available in a restricted domain, these corpora can be exploited to learn domain-specific terminology that can be used for the purpose of cross-language retrieval.
Initially, wide and restricted domains will be handled separately and with partially different methods. In the course of the project, we will examine the possibilities for automatically identifying thematically restricted subsets of open domains, and treating these with domain-specific methods.
In the following, we consider various options for cross-language retrieval, and discuss how these are applicable to the handling of open and restricted domains.
Translation of documents may be a feasible approach for a restricted domain with a limited number of documents, but it will not be feasible for general-purpose WWW search engines due to the large number of documents. In addition, there are scalability problems with the addition of new languages, since each new language would require a re-translation of documents that are already indexed, and are probably not stored with their full text.
We will therefore investigate document translation only as a technique for restricted domains, and compare its performance to (and in combination with) other techniques.
The translation of query terms entered by a user is also very problematic because these terms are often ambiguous, and a short query does not provide enough context to enable an accurate translation.
Note that using translated documents in this way requires the CLTR system to know which documents are translations of each other. This requirement is addressed by a document management system (cf. section 2.6).
A problem with using MT for text retrieval is that recall will suffer because MT systems choose only one of several possible translations. If all the possible translations produced by an MT system were added to a query in the target language, the recall could be improved.
Since the undesirable consequences of ambiguity in monolingual retrieval are compounded in cross-language retrieval, performance gains can be expected from any system that performs indexing and retrieval according to the concepts expressed in the documents and the queries rather than the words.
Adequate cross-language retrieval is therefore concept-based retrieval.
The two approaches based on relevance feedback (2.2.5 and 2.2.6) are a step in this direction since expanding a query with the translation of an entire document that is judged relevant tends to smooth out the undesirable effects of wrong translations of single query terms.
In the longer run, it will make sense to direct research and development in several directions: on the one hand towards better disambiguation of words, toward index terms that go beyond single words, and towards indexing based on grammatical relations. All of these will be briefly discussed in the following
It is an important observation that words carry different meanings (and have different translations) depending on their syntactic context. For example, if something is in a table, one is normally talking about statistical material or word processing, and table should be translated into German as Tabelle. On the other hand, if something is on a table, one is normally talking about furniture, and table should be translated into German as Tisch. Likewise the word key will usually refer to different concepts in the phrases hit a key and turn a key. Such facts have been used for the acquisition of lexical semantic knowledge from corpora [Johnston et al. 1995]. The project will use and further develop corpus-based techniques for syntactic (part of speech) and semantic disambiguation of words depending on their syntactic context and the words with which they co-occur.
With current retrieval systems based on keywords or statistical similarity, a query such as "bull kills torero" would also find documents in which the torero kills the bull, and the query "installing an operating system" would also locate documents in which the operating system installs programs, files or drivers.
The following examples show that information about installing an operating system can be expressed in a number of different syntactic forms (compounds, complex noun phrases, finite and infinitive verb phrases, gerunds etc.):
How to install the operating systemThe techniques for discovering such relationships in a text have been developed in the area of information extraction (also called "message understanding"), where the task is to extract predefined pieces of information from a text. The performance of information extraction systems is evaluated regularly in the MUC (Message Understanding Conference) competitions, in which the systems have to find information about terrorist attacks or joint ventures from newspaper articles.
Installation of the operating system
Operating system installation
Procedures for installing the operating system
The operating system is installed by ...
The techniques (shallow parsing and template filling, see section 3.7) developed for information extraction can also form a basis for information retrieval based on grammatical relations.
The project will develop special data structures for providing efficient storage of and access to indices based on grammatical relations.
The project strives to provide methods and tools for helping this kind of navigation process in an information space. Among the methods provided are established methods such as relevance feedback, thesaurus-based query expansion, but also new approaches such as partitioning the space of found documents according to criteria such as language, thematic classification, physical location etc., and letting the user choose among these subclasses.
Further opportunities for interaction with the user are in the area of the selection of word senses of ambiguous query terms, and interactive thesaurus-based query expansion and translation.
|-||by protocol (http, ftp, nntp, ...),|
|-||by location (top-level domain),|
|-||by document type (text, images, video, sound),|
|-||by date of creation of last modification,|
|-||by popularity (number of accesses), or|
Filtering according to language is not yet implemented in existing search engines, but can easily be done by using algorithms for language identification (see section 3.2) at indexing time to detect the document language, and using the language negotiation features of the HTTP 1.1 protocol to retrieve only documents in the language(s) preferred by the user. Future system will have to go beyond these more or less superficial criteria to offer more options for iteratively constraining the search to find the desired documents. It appears unreasonable to expect the user to fix thematic categories in advance of the search since there is a vast range of such categories which would be hard to learn.. We intend to perform a keyword-based search first, and then group the found documents into thematic categories for selection by the user.
|1.||If several translations of one document are retrieved by a cross-language query, they should only be shown as one "hit".|
|2.||In order to derive multilingual terminology from translated documents, it is necessary to store alignment information for translated documents.|
In addition to these requirements motivated by cross-language retrieval, there should also be tools to support the consistency of the information across different languages, and perhaps the integration of the document management systems with translators' workbenches to support the creation of multilingual web sites.
Fulcrum's software has been adopted by the Commission of the European Community and has been selected as standard information retrieval product by the European Space Agency.
At the core of Fulcrum's product family is Fulcrum SearchServer, a multi-platform indexing and retrieval server engine, which makes use of an SQL-based query language and complies with Open Database Connectivity (ODBC).
Fulcrum SurfBoard combines the SearchServer indexing engine with Internet access protocols to allow information providers to search-enable their Internet sites. World Wide Web browsers and other common Internet clients can be used to search and navigate effectively through corporate publications. Automatic conversion to HTML means that information providers do not have to invest significant resources in converting extensive document collections.
SearchServer and SurfBoard consitute the basic full-text retrieval system to which multilingual and concept-based search facilities will be added in the MULINEX project.
Two benefits will be gained from automatic language identification:
|1.||Once the language has been identified, it is possible to use the appropriate linguistic processing components for that language, for example to avoid classifying the German noun Wetter as the comparative form of the adjective wet.|
|2.||It becomes possible to inform the user in which language the retrieved documents are written, and to filter out undesired languages according to the user's preferences.|
For German, we will use the morphological analyser MONA, developed by DFKI, which has a broad coverage (more than 120.000 stem entries) and an excellent speed of 2800 words/sec on a SUN SparcStation 20. For other languages, existing commercial morphological analysers will be used.
Part-of-Speech tagging (disambiguation) is an important step in the identification of phrases. We will use an unsupervised tagger described in [Brill 1995].
TAlign combines statistical and heuristic methods to achieve optimum results. Numerous parameters adjust TAlign for specific input texts, allowing creation of as many reliably-aligned sentence pairs as possible.
Depending on the quality of the texts, TAlign can handle switched or missing paragraphs. Even if a sentence was omitted or translated by multiple sentences, TAlign can in most cases make the correct alignment decision. To support this decision process, the user has the option of specifying bilingual word lists and so-called "priority lists."
Multilingual terminology that can be used for cross-language retrieval will be extracted from the aligned texts.
Hyperwave [Maurer 1996] (formerly Hyper-G), a second-generation hypermedia system, from IICM at the Technical University of Graz makes use of multilingual document clusters, in which versions of a document in different languages are treated as one unit, and one version is delivered to the user according to his/her language preference. It is not yet clear whether Hyperwave will be used in the project, because its mechanism for language preferences has been superseded by the content negotiation of HTTP 1.1, and one of its most attractive features, a separate database for hyperlinks, is not (yet) available for HTML documents.
The multilingual HTML extension MLHTML, developed in the EU project RELATOR, which contains the different language versions of a document in one source file has been rejected as too inflexible.
Beyond the users represented in the project consortium (see section 6), a wider project user group has been established that contains a number of public institutions and commercial companies interested in the technologies and results of the project. The user group will review intermediate deliverables and prototypes, and ensure that the project produces results will be re-usable beyond the immediate requirements of the project's partners.
The feedback from the users has already led to a modification of the project's workplan: Initially, it was planned to make heavy use of a translator's workbench, and use the terminology databases and translation memories that are created during the (human) translation process for multilingual indexing. However, the users have indicated that human translation of documents does not play a major role in their development process of multilingual websites.
The project will thoroughly assess the user needs and requirements in order to develop technologies and system that satisfy real user needs. The techniques used for the assessment of user needs are described in the following:
The MULINEX prototype will be compared in performance to the best existing systems such as Fulcrum SearchServer, AltaVista and Lycos. Test suites and methodology for quantitative evaluation will be constructed as part of the quality assurance efforts of the project.
|DFKI GmbH, Saarbrücken, Germany|
|TRADOS Germany GmbH, Stuttgart, Germany,|
|Bertelsmann Telemedia GmbH, Gütersloh, Germany|
|Grolier Interactive Europe, Paris, France|
|DATAMAT - Ingegneria dei Sistemi S.p.A., Rome, Italy|
The consortium combines users (Telemedia, Grolier) and technology providers (Datamat, DFKI, Trados). The five partners bring in expertise from various areas:
The Language Technology Lab of DFKI (German Research Center for Artificial Intelligence) brings in experience in Natural-Language Processing, especially morphological analysis, syntactic analysis and message extraction.
DATAMAT develops state-of-the-art information retrieval technology through its subsidiary Fulcrum. The Fulcrum SearchServer is described in section 3.1.
TRADOS develops software products in the field of translation tools. TRADOS specialises in terminology database systems and translation memory systems, and tools for alignment of text and terminology extraction.
The role of users in the project is played by Grolier Interactive Europe and Bertelsmann Telemedia. Grolier is dedicated to the development of multimedia and interactive communications online, especially focussed on the World Wide Web. Telemedia is the Internet solutions company of Bertelsmann AG, and offers professional service in all areas connected with the WWW, especially electronic commerce.