ACL-IJCNLP 2015
TechTalks from event: ACL-IJCNLP 2015
session 3A Language Resources
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a new corpus and imitation learning framework for context-dependent semantic parsingSemantic parsing is the task of translating natural language utterances into a machine-interpretable meaning representation. Most approaches to this task have been evaluated on a small number of existing corpora which assume that all utterances must be interpreted according to a database and typically ignore context. In this paper we present a new, publicly available corpus for context-dependent semantic parsing. The MRL used for the annotation was designed to support a portable, interactive tourist information system. We develop a semantic parser for this corpus by adapting the imitation learning algorithm DAGGER without requiring alignment information during training. DAGGER improves upon independently trained classifiers by 9.0 and 4.8 points in F-score on the development and test sets respectively.
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it depends dependency parser comparison using a web-based evaluation toolThe last few years have seen a surge in the number of accurate, fast, publicly available dependency parsers. At the same time, the use of dependency parsing in NLP applications has increased. It can be difficult for a non-expert to select a good ``off-the-shelf'' parser. We present a comparative analysis of nine leading statistical dependency parsers on a multi-genre corpus of English. For our analysis, we developed a new web-based tool that gives a convenient way of comparing dependency parser outputs. Our analysis will help practitioners choose a parser to optimize their desired speed/accuracy tradeoff, and our tool will help practitioners examine and compare parser output.
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generating high quality proposition banks for multilingual semantic role labelingSemantic role labeling (SRL) is crucial to natural language understanding as it identifies the predicate-argument structure in text with semantic labels. Unfortunately, resources required to construct SRL models are expensive to obtain and simply do not exist for most languages. In this paper, we present a two-stage method to enable the construction of SRL models for resource-poor languages by exploiting monolingual SRL and multilingual parallel data. Experimental results show that our method outperforms existing methods. We use our method to generate Proposition Banks with high to reasonable quality for 7 languages in three language families and release these resources to the research community.
- All Sessions
- SessionName
- tutorials T1
- tutorials T5
- tutorials T2
- tutorials T6
- tutorials T3
- tutorials T7
- tutorials T4
- tutorials T8
- session 1B Language and Vision/NLP Applications
- session 2A Machine Translation
- session 3A Language Resources
- session 4A Semantics
- session 2B Question Answering
- session 3B Sentiment Analysis: Cross-/Multi Lingual
- session 4B Sentiment Analysis
- session 1C Semantics: Embeddings
- session 2C Semantics: Distributional Approaches
- session 3C Natural Language Generation
- session 4C Summarization and Generation
- session 1D Machine Learning
- session 2D Parsing: Neural Networks
- session 3D Spoken Language Processing and Understanding
- session 4D Discourse, Coreference
- session 1E Information Extraction 1
- session 2E Information Extraction 2
- session 3E Information Extraction 3/Information Retrieval
- session 4E Language and Vision
- session 1 Machine Translation: Neural Networks
- president talk
- session 5B Machine Learning and Topic Modeling
- session 6A Discourse, Pragmatics
- session 7A Discourse, Coreference
- student research workshop
- session 5C Semantics, Linguistic and Psycholinguistic Aspects of CL
- session 6C Semantics: Semantic Parsing
- session 7C Semantics: Semantic Parsing
- session 6B Machine Learning: Embeddings
- session 7B Topic Modeling
- session 7D Lexical Semantics
- session 6D Sentiment Analysis: Learning
- session 5D Parsing, Tagging
- session 5E Information Extraction
- session 6E Grammar Induction and Annotation
- session 7E Parsing
- invited talk
- session 5A Machine Translation
- session 8B Automatic Summarization
- session 9B Word Segmentation
- session 8C Linguistic and Psycholinguistic Aspects of NLP
- session 9C Morphology, Phonology
- session 8D NLP for the Web: Social Media
- session 9D NLP for the Web: Twitter
- session 8E Text Categorization/Information Retrieval
- session 9E POS Tagging
- session 8A Machine Learning: Neural Networks
- session 9A Multilinguality
- session BP Best Paper Session