Name
Abstract | ||
|---|---|---|
Retrieving information that is implicit in a text is difficult. For argument analysis, revealing implied knowledge could be useful to judge how solid an argument is and to construct concise arguments. We design a process for obtaining high-quality implied knowledge annotations for German argumentative microtexts, in the form of simple natural language statements. This process involves several steps to promote agreement and monitors its evolution using textual similarity computation. To further characterize the implied knowledge, we annotate the added sentences with semantic clause types and common sense knowledge relations. To test whether the knowledge could be retrieved automatically, we compare the inserted sentences to Wikipedia articles on similar topics. Analysis of the added knowledge shows that (i) it is characterized by a high proportion of generic sentences, (ii) a majority of it can be mapped to common sense knowledge relations, and (iii) it is similar to sentences found in Wikipedia. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59569-6_9 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Argumentation,Implicit knowledge,Annotation process,Textual similarity,Semantic clause types,Common sense knowledge,Wikipedia | Commonsense knowledge,Argumentative,Similarity computation,Computer science,Implicit knowledge,Argumentation theory,Natural language,Artificial intelligence,Natural language processing,Linguistics,German | Conference |
Volume | ISSN | Citations |
10260 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Becker | 1 | 1 | 1.03 |
| Michael Staniek | 2 | 0 | 0.34 |
| Vivi Nastase | 3 | 523 | 41.30 |
| Anette Frank | 4 | 63 | 11.94 |