Linking Models of Lexical, Sentential and Discourse-level Semantics
Тези до: 28.06.2015
Дати: 18.09.15 — 18.09.15
Область наук: Філологічні;
Е-мейл Оргкомітету: firstname.lastname@example.org
Організатори: SIGLEX, SIGSEM and SIGdial
Improved computational models of semantics hold great promise for applications in language technology, be it semantics at the lexical level, sentence level or discourse level. Large-scale corpora with corresponding annotations (word senses, propositions, attributions and discourse relations) are making it possible to develop statistical models for many tasks and applications. However, developments in lexical and sentence-level semantics remain largely distinct from those in discourse semantics. This workshop aims at bridging this gap by bringing together researchers to discuss how multiple levels of semantics can be integrated and implemented in various applications.
Early linguistic studies considered the interplay between discourse context and meaning at the sentence level, showing inter alia that information structure impacts syntactic realization and that lexical and pragmatics factors affect the realization of verb arguments. Although early computational work involving semantics took some account of discourse, modern approaches to semantic processing are only now beginning to consider it: semantic parsing with context, predicting argument realization and linking implicit arguments, disambiguating words using author and discourse information, and classifying paradigmatic relations between words.
It is clear that interactions between semantic phenomena within sentences and on the discourse level are not unidirectional: Factors such as modality, negation and syntactic patterns have shown to correlate strongly with the sense of discourse relations. Similarly, situational entities mentioned within a sentence can indicate specific discourse modes. Complementing discourse-aware models of semantics are semantically-informed methods for text-level processing, such as discourse parsers that rely on models of rich lexical representations.
Many applications of natural language processing can benefit from bringing together models of semantics on the word, sentence and discourse level. Recent advances can be seen for instance in recognizing textual entailment, sentiment analysis, information extraction, event ordering, and question answering. Besides traditional applications of natural language processing, researchers in cognitive science have started to explore different levels of representation for computing semantic similarity between paragraphs and between documents.
This workshop aims to gather and showcase theoretical and computational approaches to joint models of semantics, and applications that incorporate multi-level semantics. We hope to bring together researchers from various areas: linguists and cognitive scientists working on aspects of representing text with multiple levels of semantics, machine learning researchers interested in joint inference over different types of semantic cues, and also researchers who are interested in applications that require or will benefit from multi-level semantics. A dialog between researchers has great potential to advance work in each of these areas and bring about more powerful and enriched models of text semantics.
Topics of interest include, but are not limited to:
Approaches for creating models of lexical and sentence semantics enriched with discourse information
Sentence-level semantic processing tasks such as SRL, paraphrase detection, semantic parsing that take account of discourse context
Integration of rich sentence semantics into discourse tasks such as discourse parsing, coherence modeling, coreference resolution, text segmentation (by topic and/or by function)
Joint models of sentence and discourse semantics
Applications, such as summarization, text generation and question answering, developed based on multiple layers of semantic information
Веб-сторінка конференції: http://homepages.inf.ed.ac.uk/mroth/LSDSem/
Конференції по темі - із близькими дедлайнами:
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