Christian Biemann

Disentangling from babylonian confusion – unsupervised language identification

This work presents an unsupervised solution to language identification. The method sorts multilingual text corpora on the basis of sentences into the different languages that are contained and makes no assumptions on the number or size of the monolingual fractions. Evaluation on 7-lingual corpora and bilingual corpora show that the quality of classification is comparable to supervised approaches and works almost error-free from 100 sentences per language on.

Language-Independent Methods for Compiling Monolingual Lexical Data

In this paper we describe a flexible, portable and language-independent infrastructure for setting up large monolingual language corpora. The approach is based on collecting a large amount of monolingual text from various sources. The input data is processed on the basis of a sentence-based text segmentation algorithm. We describe the entry structure of the corpus database as well as various query types and tools for information extraction. Among them, the extraction and usage of sentence-based word collocations is discussed in detail. Finally we give an overview of different applications for this language resource. A WWW interface allows for public access to most of the data and information extraction tools (http://wortschatz.uni-leipzig.de).

Automatically building concept structures and displaying concept trails for the use in brainstorming sessions and content management systems

The automated creation and the visualization of concept structures become more important as the number of relevant information continues to grow dramatically. Especially information and knowledge intensive tasks are relying heavily on accessing the relevant information or knowledge at the right time. Moreover the capturing of relevant facts and good ideas should be focused on as early as possible in the knowledge creation process. In this paper we introduce a technology to support knowledge structuring processes already at the time of their creation by building up concept structures in real time. Our focus was set on the design of a minimal invasive system, which ideally requires no human interaction and thus gives the maximum freedom to the participants of a knowledge creation or exchange processes. The initial prototype concentrates on the capturing of spoken language to support meetings of human experts, but can be easily adapted for the use in Internet communities that have to rely on knowledge exchange using electronic communication channels.

Named entity learning and verification: expectation maximization in large corpora

The regularity of named entities is used to learn names and to extract named entities. Having only a few name elements and a set of patterns the algorithm learns new names and its elements. A verification step assures quality using a large background corpus. Further improvement is reached through classifying the newly learnt elements on character level. Moreover, unsupervised rule learning is discussed.

SemanticTalk: software for visualizing brainstorming sessions and thematic concept trails on document collections

In this demonstration we introduce a technology to support knowledge structuring processes already at the time of their creation by building up concept structures in real time. Our focus was set on the design of a minimal invasive system, which ideally requires no human interaction and thus gives the maximum freedom to the participants of a knowledge creation or exchange processes. The system captures and displays spoken dialogs as well as text documents for further use in knowledge engineers tools.