Refine
Document Type
- Part of a Book (1)
- Conference Proceeding (1)
- Doctoral Thesis (1)
Has Fulltext
- yes (3)
Keywords
- computational linguistics (3) (remove)
Publicationstate
Reviewstate
Publisher
In this paper we investigate the problem of grammar inference from a different perspective. The common approach is to try to infer a grammar directly from example sentences, which either requires a large training set or suffers from bad accuracy. We instead view it as a problem of grammar restriction or sub-grammar extraction. We start from a large-scale resource grammar and a small number of examples, and find a sub-grammar that still covers all the examples. To do this we formulate the problem as a constraint satisfaction problem, and use an existing constraint solver to find the optimal grammar. We have made experiments with English, Finnish, German, Swedish and Spanish, which show that 10–20 examples are often sufficient to learn an interesting domain grammar. Possible applications include computer-assisted language learning, domain-specific dialogue systems, computer games, Q/A-systems, and others.
Sentiment Analysis is the task of extracting and classifying opinionated content in natural language texts. Common subtasks are the distinction between opinionated and factual texts, the classification of polarity in opinionated texts, and the extraction of the participating entities of an opinion(-event), i.e. the source from which an opinion emanates and the target towards which it is directed. With the emerging Web 2.0 which describes the shift towards a highly user-interactive communication medium, the amount of subjective content on the World Wide Web is steadily increasing. Thus, there is a growing need for automatically processing this type of content which is provided by sentiment analysis. Both natural language processing, which is the task of providing computational methods for the analysis and representation of natural language, and machine learning, which is the task of building task-specific classification models on the basis of empirical data, may be instrumental in mastering the challenges of the automatic sentiment analysis of written text. Many problems in sentiment analysis have been proposed to be solved with machine learning methods exclusively using a fairly low-level feature design, such as bag of words, containing little linguistic information. In this thesis, we examine the effectiveness of linguistic features in various subtasks of sentiment analysis. Thus, we heavily draw from the insights gained by natural language processing. The application of linguistic features can be applied on various classification methods, be it in rule-based classification, where the linguistic features are directly encoded as a classifier, in supervised machine learning, where these features complement basic low-level features, or in bootstrapping methods, where these features form a rule-based classifier generating a labeled training set from which a supervised classifier can be trained. In this thesis, we will in particular focus on scenarios where the combination of linguistic features and machine learning methods is effective. We will look at common text classification tasks, both coarse-grained and fine-grained, and extraction tasks.
Ulrich Engel hat mit seinen Publikationen zur deutschen Grammatik, zur Verbvalenz und zur kontrastiven Linguistik große Wirkung auf die internationale germanistische Linguistik ausgeübt. Weniger bekannt ist, dass er mit seinem Werk auch andere linguistische Teildisziplinen beeinflusst hat, die davon bis heute profitieren. Dependenzielle Ansätze spielen bei der maschinellen Syntaxanalyse mittlerweile eine zentrale Rolle, und bei der Entwicklung von Systemen zur maschinellen Übersetzung haben Engels Arbeiten ebenfalls ihre Spur hinterlassen. Der Aufbau von Sprachressourcen in Gestalt von „Baumbanken“ kann auf Engels Grammatikkonzeption zurückgreifen, und auch zur neuerlich florierenden Konstruktionsgrammatik bestehen klare Bezüge. Im Beitrag werden diese weniger bekannten Einwirkungen von Engels Werk in andere Bereiche dargestellt und in ihrer andauernden Aktualität gewürdigt.