Automatic adaptive understanding of spoken language by cooperation of syntactic parsing and semantic priming
نویسندگان
چکیده
This paper focuses on the modelling of the linguistic level of MICRO, a multi-agents speech understanding system largely inspired by cognitive models. It describes the cooperation between, on the one hand a syntactic parser using a Lexical Functional Grammar, and on the other hand an semantic analyser. The semantic analysis is achieved through a mechanism semantic priming carried out by a incremental associative network. We emphasize the adaptive abilities of such a cooperation, particularly in case of ungrammatical utterances, which are very common in spoken language.
منابع مشابه
برچسبزنی خودکار نقشهای معنایی در جملات فارسی به کمک درختهای وابستگی
Automatic identification of words with semantic roles (such as Agent, Patient, Source, etc.) in sentences and attaching correct semantic roles to them, may lead to improvement in many natural language processing tasks including information extraction, question answering, text summarization and machine translation. Semantic role labeling systems usually take advantage of syntactic parsing and th...
متن کاملSyntactic annotation of spontaneous speech: application to call-center conversation data
Both frameworks are based on the automatic semantic analysis of Human-Human spoken conversations. The semantic interpretation of a spoken utterance can be split into a two-level process: a tagging process projecting lexical items into basic conceptual constituents and a composition process that takes as input these basic constituents and combine them in a possibly complex semantic interpretatio...
متن کاملJoint Syntactic and Semantic Analysis with a Multitask Deep Learning Framework for Spoken Language Understanding
Spoken Language Understanding (SLU) models have to deal with Automatic Speech Recognition outputs which are prone to contain errors. Most of SLU models overcome this issue by directly predicting semantic labels from words without any deep linguistic analysis. This is acceptable when enough training data is available to train SLU models in a supervised way. However for open-domain SLU, such anno...
متن کاملAdapting dependency parsing to spontaneous speech for open domain spoken language understanding
Parsing human-human conversations consists in automatically enriching text transcription with semantic structure information. We use in this paper a FrameNet-based approach to semantics that, without needing a full semantic parse of a message, goes further than a simple flat translation of a message into basic concepts. FrameNet-based semantic parsing may follow a syntactic parsing step, howeve...
متن کاملSemantic Priming Effect on Relative Clause Attachment Ambiguity Resolution in L2
This study examined whether processing ambiguous sentences containing relative clauses (RCs) following a complex determiner phrase (DP) by Persian-speaking learners of L2 English with different proficiency and working memory capacities (WMCs) is affected by semantic priming. The semantic relationship studied was one between the subject/verb of the main clause and one of the DPs in the complex D...
متن کامل