This paper presents an approach for detecting semantic relations in noun phrases. A learning algorithm, called semantic scattering, is used to automatically label complex nominals, genitives and adjectival noun phrases with the corresponding semantic relation.

The discovery of semantic relations from text becomes increasingly important for applications such as Question Answering, Information Extraction, Text Summarization, Text Understanding, and others. The semantic relations are detected by checking selectional constraints. This paper presents a method and its results for learning semantic constraints to detect part-whole relations. Twenty constraints were found. Their validity was tested on a 10,000 sentence corpus, and the targeted partwhole relations were detected with an accuracy of 83%.

An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper presents a supervised, semantically intensive, domain independent approach for the automatic detection of part-whole relations in text. First an algorithm is described that identifies lexico-syntactic patterns that encode part-whole relations. A difficulty is that these patterns also encode other semantic relations, and a learning method is necessary to discriminate whether or not a pattern contains a part-whole relation. A large set of training examples have been annotated and fed into a specialized learning system that learns classification rules. The rules are learned through an iterative semantic specialization (ISS) method applied to noun phrase constituents. Classification rules have been generated this way for different patterns such as genitives, noun compounds, and noun phrases containing prepositional phrases to extract part-whole relations from them. The applicability of these rules has been tested on a test corpus obtaining an overall average precision of 80.95% and recall of 75.91%. The results demonstrate the importance of word sense disambiguation for this task. They also demonstrate that different lexico-syntactic patterns encode different semantic information and should be treated separately in the sense that different clarification rules apply to different patterns.

This paper addresses the automatic classification of the semantic relations expressed by the English genitives. A learning model is introduced based on the statistical analysis of the distribution of genitives' semantic relations on a large corpus. The semantic and contextual features of the genitive's noun phrase constituents play a key role in the identification of the semantic relation. The algorithm was tested on a corpus of approximately 2,000 sentences and achieved an accuracy of 79% , far better than 44% accuracy obtained with C5.0, or 43% obtained with a Naive Bayes algorithm, or 27% accuracy with a Support Vector Machines learner on the same corpus.

An important problem in knowledge discovery from text is the automatic extraction of semantic relations. This paper addresses the automatic classification of the semantic relations expressed by English genitives. A learning model is introduced based on the statistical analysis of the distribution of genitives' semantic relations in a corpus. The semantic and contextual features of the genitive's noun phrase constituents play a key role in the identification of the semantic relation. The algorithm was trained and tested on a corpus of approximately 20,000 sentences and achieved an f-measure of 79.80 per cent for of-genitives, far better than the 40.60 per cent obtained using a Decision Trees algorithm, the 50.55 per cent obtained using a Naive Bayes algorithm, or the 72.13 per cent obtained using a Support Vector Machines algorithm on the same corpus using the same features. The results were similar for s-genitives: 78.45 per cent using Semantic Scattering, 47.00 per cent using Decision Trees, 43.70 per cent using Naive Bayes, and 70.32 per cent using a Support Vector Machines algorithm. The results demonstrate the importance of word sense disambiguation and semantic generalization/specialization for this task. They also demonstrate that different patterns (in our case the two types of genitive constructions) encode different semantic information and should be treated differently in the sense that different models should be built for different patterns.