Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable information that is contained in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a method for refining vector space representations using relational information from semantic lexicons by encouraging linked words to have similar vector representations, and it makes no assumptions about how the input vectors were constructed. Evaluated on a battery of standard lexical semantic evaluation tasks in several languages, we obtain substantial improvements starting with a variety of word vector models. Our refinement method outperforms prior techniques for incorporating semantic lexicons into the word vector training algorithms.
The approach we propose is conceptually similar to previous work that uses graph structures to propagate information among semantic concepts @cite_9 @cite_18 . Graph-based belief propagation has also been used to induce POS tags @cite_34 @cite_31 and semantic frame associations @cite_24 . In those efforts, labels for unknown words were inferred using a method similar to ours. Broadly, graph-based semi-supervised learning @cite_9 @cite_38 has been applied to machine translation @cite_19 , unsupervised semantic role induction @cite_27 , semantic document modeling @cite_36 , language generation @cite_22 and sentiment analysis @cite_13 .
@cite_38 @cite_18 @cite_22 @cite_36 @cite_9 @cite_24 @cite_19 @cite_27 @cite_31 @cite_34 @cite_13
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We describe a new approach to disambiguating semantic frames evoked by lexical predicates previously unseen in a lexicon or annotated data. Our approach makes use of large amounts of unlabeled data in a graph-based semi-supervised learning framework. We construct a large graph where vertices correspond to potential predicates and use label propagation to learn possible semantic frames for new ones. The label-propagated graph is used within a frame-semantic parser and, for unknown predicates, results in over 15 absolute improvement in frame identification accuracy and over 13 absolute improvement in full frame-semantic parsing F1 score on a blind test set, over a state-of-the-art supervised baseline. Unknown lexical items present a major obstacle to the development of broad-coverage semantic role labeling systems. We address this problem with a semi-supervised learning approach which acquires training instances for unseen verbs from an unlabeled corpus. Our method relies on the hypothesis that unknown lexical items will be structurally and semantically similar to known items for which annotations are available. Accordingly, we represent known and unknown sentences as graphs, formalize the search for the most similar verb as a graph alignment problem and solve the optimization using integer linear programming. Experimental results show that role labeling performance for unknown lexical items improves with training data produced automatically by our method. We describe a novel approach for inducing unsupervised part-of-speech taggers for languages that have no labeled training data, but have translated text in a resource-rich language. Our method does not assume any knowledge about the target language (in particular no tagging dictionary is assumed), making it applicable to a wide array of resource-poor languages. We use graph-based label propagation for cross-lingual knowledge transfer and use the projected labels as features in an unsupervised model (Berg-, 2010). Across eight European languages, our approach results in an average absolute improvement of 10.4 over a state-of-the-art baseline, and 16.7 over vanilla hidden Markov models induced with the Expectation Maximization algorithm. We describe an approach for performing entity type recognition in heterogeneous semantic graphs in order to reduce the computational cost of performing coreferenceresolution. Our research specifically addresses the problem of working with semi-structured text that uses ontologies that are not informative or not known. This problem is similar to co reference resolution in unstructured text, where entities and their types are identified using contextual information and linguistic-based analysis. Semantic graphs are semi-structured with very little contextual information and trivial grammars that do not convey additional information. In the absence of known ontologies, performing co reference resolution can be challenging. Our work uses a supervised machine learning algorithm and entity type dictionaries to map attributes to a common attribute space. We evaluated the approach in experiments using data from Wikipedia, Freebase and Arnetminer.