Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

Knowledge extraction and representation aims to identify information and to transform it into a machine-readable format. Knowledge representations support Information Retrieval tasks such as searching for single statements, documents, or metadata. Requirements specifications of complex systems such as automotive software systems are usually divided into different subsystem specifications. Nevertheless, there are semantic relations between individual documents of the separated subsystems, which have to be considered in further processes (e.g. dependencies). If requirements engineers or other developers are not aware of these relations, this can lead to inconsistencies or malfunctions of the overall system. Therefore, there is a strong need for tool support in order to detects semantic relations in a set of large natural language requirements specifications. In this work we present a knowledge extraction approach based on an explicit knowledge representation of the content of natural language requirements as a semantic relation graph. Our approach is fully automated and includes an NLP pipeline to transform unrestricted natural language requirements into a graph. We split the natural language into different parts and relate them to each other based on their semantic relation. In addition to semantic relations, other relationships can also be included in the graph. We envision to use a semantic search algorithm like spreading activation to allow users to search different semantic relations in the graph

Building a Generation Knowledge Source using Internet-Accessible Newswire

In this paper, we describe a method for automatic creation of a knowledge source for text generation using information extraction over the Internet. We present a prototype system called PROFILE which uses a client-server architecture to extract noun-phrase descriptions of entities such as people, places, and organizations. The system serves two purposes: as an information extraction tool, it allows users to search for textual descriptions of entities; as a utility to generate functional descriptions (FD), it is used in a functional-unification based generation system. We present an evaluation of the approach and its applications to natural language generation and summarization.Comment: 8 pages, uses eps

Extracting Noun Phrases from Large-Scale Texts: A Hybrid Approach and Its Automatic Evaluation

To acquire noun phrases from running texts is useful for many applications, such as word grouping,terminology indexing, etc. The reported literatures adopt pure probabilistic approach, or pure rule-based noun phrases grammar to tackle this problem. In this paper, we apply a probabilistic chunker to deciding the implicit boundaries of constituents and utilize the linguistic knowledge to extract the noun phrases by a finite state mechanism. The test texts are SUSANNE Corpus and the results are evaluated by comparing the parse field of SUSANNE Corpus automatically. The results of this preliminary experiment are encouraging.Comment: 8 pages, Postscript file, Unix compressed, uuencode

Learning Analogies and Semantic Relations

We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the Scholastic Aptitude Test (SAT). A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct). We motivate this research by relating it to work in cognitive science and linguistics, and by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as "laser printer", according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for these challenging problems