A Concept for a Prosodically and Statistically Driven Chunky Semantic Parser
نویسندگان
چکیده
(Czech Republic), 1998, (pp. 357{362). A Concept for a Prosodically and Statistically Driven Chunky Semantic Parser J urgen Haas, Manuela Boros, Elmar N oth, Volker Warnke, Heinrich Niemann University of Erlangen-N urnberg Chair for Pattern Recognition { Martensstra e 3 { 91058 Erlangen { Germany (haas,boros,noeth,warnke,niemann)@informatik.uni-erlangen.de Abstract. In spoken dialog systems typically just a small set of predened information has to be provided to the system in order to accomplish its task. We present here a concept for a partial (chunky) semantic parser, whose task is to detect the parts in a user utterance that contain needed information and to analyze these parts linguistically. In order to support the parser, we introduce statistical and prosodic methods to predict semantic concepts in an utterance und their location.
منابع مشابه
Feature Engineering in Persian Dependency Parser
Dependency parser is one of the most important fundamental tools in the natural language processing, which extracts structure of sentences and determines the relations between words based on the dependency grammar. The dependency parser is proper for free order languages, such as Persian. In this paper, data-driven dependency parser has been developed with the help of phrase-structure parser fo...
متن کاملOn building phonetically and prosodically rich speech corpus for text-to-speech synthesis
This paper proposes a way of preparing and recording a speech corpus for unit selection text-to-speech speech synthesis driven by symbolic prosody. The research is focused on a phonetically and prosodically rich sentence selection algorithm. Symbolic description on a deep prosody level is used to enrich the phonetic representation of sentences (by respecting the prosodeme types phones appear in...
متن کاملHow Statistics and Prosody can guide aChunky
Chunky Parser Manuela Boros1, J urgen Haas2, Elmar N oth2, Volker Warnke2, Heinrich Niemann2 1 Bavarian Research Center for Knowledge Based Systems (FORWISS) Am Weichselgarten 7 D-91058 Erlangen Germany email: [email protected] 2 University of Erlangen-N urnberg Chair for Pattern Recognition Martensstra e 3 D-91058 Erlangen Germany email: (haas,noeth,warnke,niemann)@informatik.un...
متن کاملبرچسبزنی نقش معنایی جملات فارسی با رویکرد یادگیری مبتنی بر حافظه
Abstract Extracting semantic roles is one of the major steps in representing text meaning. It refers to finding the semantic relations between a predicate and syntactic constituents in a sentence. In this paper we present a semantic role labeling system for Persian, using memory-based learning model and standard features. Our proposed system implements a two-phase architecture to first identify...
متن کاملبرچسبزنی 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 کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998