Query-Based Summarization using Rhetorical Structure Theory

Research on Question Answering is focused mainly on classifying the question type and finding the answer. Presenting the answer in a way that suits the user’s needs has received little attention. This paper shows how existing question answering systems—which aim at finding precise answers to questions—can be improved by exploiting summarization techniques to extract more than just the answer from the document in which the answer resides. This is done using a graph search algorithm which searches for relevant sentences in the discourse structure, which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a graph representation of a text document. The output is an extensive answer, which not only answers the question, but also gives the user an opportunity to assess the accuracy of the answer (is this what I am looking for?), and to find additional information that is related to the question, and which may satisfy an information need. This has been implemented in a working multimodal question answering system where it operates with two independently developed question answering modules

Structural parsing

Parsing is an essential part of natural language processing. In this paper, structural parsing, which is based on the theory of knowledge graphs, is introduced. Under consideration of the semantic and syntactic features of natural language, both semantic and syntactic word graphs are formed. Grammar rules are derived from the syntactic word graphs. Due to the distinctions between Chinese and English, the grammar rules are given for the Chinese version and the English version of syntactic word graphs respectively. By traditional parsing a parse tree can then be given for a sentence, that can be used to map the sentence on a sentence graph. This is called structural parsing. The relationship with utterance paths is discussed. As a result, chunk indicators are proposed to guide structural parsing

An approach to graph-based analysis of textual documents

In this paper a new graph-based model is proposed for the representation of textual documents. Graph-structures are obtained from textual documents by making use of the well-known Part-Of-Speech (POS) tagging technique. More specifically, a simple rule-based (re) classifier is used to map each tag onto graph vertices and edges. As a result, a decomposition of textual documents is obtained where tokens are automatically parsed and attached to either a vertex or an edge. It is shown how textual documents can be aggregated through their graph-structures and finally, it is shown how vertex-ranking methods can be used to find relevant tokens.(1)

Information extraction

In this paper we present a new approach to extract relevant information by knowledge graphs from natural language text. We give a multiple level model based on knowledge graphs for describing template information, and investigate the concept of partial structural parsing. Moreover, we point out that expansion of concepts plays an important role in thinking, so we study the expansion of knowledge graphs to use context information for reasoning and merging of templates

Intelligent multimedia indexing and retrieval through multi-source information extraction and merging

This paper reports work on automated meta-data\ud creation for multimedia content. The approach results\ud in the generation of a conceptual index of\ud the content which may then be searched via semantic\ud categories instead of keywords. The novelty\ud of the work is to exploit multiple sources of\ud information relating to video content (in this case\ud the rich range of sources covering important sports\ud events). News, commentaries and web reports covering\ud international football games in multiple languages\ud and multiple modalities is analysed and the\ud resultant data merged. This merging process leads\ud to increased accuracy relative to individual sources

Semantics of nouns and nominal number

In the present paper, I will discuss the semantic structure of nouns and nominal number markers. In particular, I will discuss the question if it is possible to account for the syntactic and semantic formation of nominals in a parallel way, that is I will try to give a compositional account of nominal semantics. The framework that I will use is "twolevel semantics". The semantic representations and their type-theoretical basis will account for general cross-linguistic characteristics of nouns and nominal number and will show interdependencies between noun classes, number marking and cardinal constructions. While the analysis will give a unified account of bare nouns (like dog / water), it will distinguish between the different kinds of nominal terms (like a dog / dogs / water). Following the proposal, the semantic operations underlying the formation of the SR are basically the same for DPs as for CPs. Hence, from such an analysis, independent semantic arguments can be derived for a structural parallelism of nominals and sentences - that is, for the "sentential aspect" of noun phrases. I will first give a sketch of the theoretical background. I will then discuss the cross-linguistic combinatorial potential of nominal constructions, that is, the potential of nouns and number markers to combine with other elements and form complex expressions. This will lead to a general type-theoretical classification for the elements in question. In the next step, I will model the referential potential of nominal constructions. Together with the combinatorial potential, this will give us semantic representations for the basic elements involved in nominal constructions. In an overview, I will summarize our modeling of nouns and nominal number. I will then discuss in an outlook the "sentential aspect" of noun phrases