| Abstract | ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data. |
| Abstract | Word sense induction is performed by inferring a set of semantic types for each noun phrase. |
| Abstract | When ConceptResolver is run on N ELL’s knowledge base, 87% of the word senses it creates correspond to real-world concepts, and 85% of noun phrases that it suggests refer to the same concept are indeed synonyms. |
| Introduction | Induce Word Senses i. |
| Introduction | Cluster word senses with semantic type C using classifier’s predictions. |
| Introduction | It first performs word sense induction, using the extracted category instances to create one or more unambiguous word senses for each noun phrase in the knowledge base. |
| Introduction | Active learning has been applied to several NLP tasks like part-of—speech tagging (Ringger et al., 2007), chunking (Ngai and Yarowsky, 2000), syntactic parsing (Osborne and Baldridge, 2004; Hwa, 2004), Named Entity Recognition (Shen et al., 2004; Laws and Schutze, 2008; Tomanek and Hahn, 2009), Word Sense Disambiguation (Chen et al., 2006; Zhu and Hovy, 2007; Chan and Ng, 2007), text classification (Tong and Koller, 1998) or statistical machine translation (Haffari and Sarkar, 2009), and has been shown to reduce the amount of annotated data needed to achieve a certain classifier performance, sometimes by as much as half. |
| Related Work | sentiment analysis, the detection of metaphors, WSD with fine-grained word senses , to name but a few). |
| Related Work | Table 1: Distribution of word senses in pool and test sets |
| Related Work | The different word senses are evenly distributed over the rejected instances (H1: Commitment 30, drohenl-salsa 38, Run_risk 36; H2: Commitment 3, drohenl-salsa 4, Run_risk 4). |
| Abstract | Recent work on bilingual Word Sense Disambiguation (WSD) has shown that a resource deprived language (L1) can benefit from the annotation work done in a resource rich language (L2) via parameter projection. |
| Conclusion | We presented a bilingual bootstrapping algorithm for Word Sense Disambiguation which allows two resource deprived languages to mutually benefit |
| Parameter Projection | (2009) proposed that the various parameters essential for domain-specific Word Sense Disambiguation can be broadly classified into two categories: |
| Related Work | Bootstrapping for Word Sense Disambiguation was first discussed in (Yarowsky, 1995). |