Topic
Phrase
About: Phrase is a research topic. Over the lifetime, 12580 publications have been published within this topic receiving 317823 citations. The topic is also known as: syntagma & phrases.
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01 Jan 1984TL;DR: This paper revisited the acquisition theory and language learnability and language devlopment revisited in the context of lexical entries and lexical rules, assuming and postulating phrase structure rules phrase stucture rules - developmental considerations inflection complementation and control auxiliaries lexical entry and lexitional rules.
Abstract: Language learnability and language devlopment revisited the acquisition theory - assumptions and postulates phrase structure rules phrase stucture rules - developmental considerations inflection complementation and control auxiliaries lexical entries and lexical rules.
1,978 citations
06 Jul 2002
TL;DR: In this article, an unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended(thumbs down) is presented. But the classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs.
Abstract: This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
1,904 citations
TL;DR: This review argues that sentence processing is supported by a temporo-frontal network, within this network, temporal regions subserve aspects of identification and frontal regions the building of syntactic and semantic relations.
1,760 citations
01 Sep 1997
TL;DR: A tutorial on the design and development of automatic speaker-recognition systems is presented and a new automatic speakers recognition system is given that performs with 98.9% correct decalcification.
Abstract: A tutorial on the design and development of automatic speaker-recognition systems is presented. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. These systems can operate in two modes: to identify a particular person or to verify a person's claimed identity. Speech processing and the basic components of automatic speaker-recognition systems are shown and design tradeoffs are discussed. Then, a new automatic speaker-recognition system is given. This recognizer performs with 98.9% correct decalcification. Last, the performances of various systems are compared.
1,686 citations
TL;DR: A system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame, based on statistical classifiers trained on roughly 50,000 sentences that were hand-annotated with semantic roles by the FrameNet semantic labeling project.
Abstract: We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Given an input sentence and a target word and frame, the system labels constituents with either abstract semantic roles, such as AGENT or PATIENT, or more domain-specific semantic roles, such as SPEAKER, MESSAGE, and TOPIC.The system is based on statistical classifiers trained on roughly 50,000 sentences that were hand-annotated with semantic roles by the FrameNet semantic labeling project. We then parsed each training sentence into a syntactic tree and extracted various lexical and syntactic features, including the phrase type of each constituent, its grammatical function, and its position in the sentence. These features were combined with knowledge of the predicate verb, noun, or adjective, as well as information such as the prior probabilities of various combinations of semantic roles. We used various lexical clustering algorithms to generalize across possible fillers of roles. Test sentences were parsed, were annotated with these features, and were then passed through the classifiers.Our system achieves 82% accuracy in identifying the semantic role of presegmented constituents. At the more difficult task of simultaneously segmenting constituents and identifying their semantic role, the system achieved 65% precision and 61% recall.Our study also allowed us to compare the usefulness of different features and feature combination methods in the semantic role labeling task. We also explore the integration of role labeling with statistical syntactic parsing and attempt to generalize to predicates unseen in the training data.
1,666 citations