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.

A Unified Model of Phrasal and Sentential Evidence for Information Extraction

Abstract: Information Extraction (IE) systems that extract role fillers for events typically look at the local context surrounding a phrase when deciding whether to extract it. Often, however, role fillers occur in clauses that are not directly linked to an event word. We present a new model for event extraction that jointly considers both the local context around a phrase along with the wider sentential context in a probabilistic framework. Our approach uses a sentential event recognizer and a plausible role-filler recognizer that is conditioned on event sentences. We evaluate our system on two IE data sets and show that our model performs well in comparison to existing IE systems that rely on local phrasal context.

The progressive in English: Events, states and processes

Abstract: This paper has two goals. The first is to formulate an adequate account of the semantics of the progressive aspect in English: the semantics of ‘Agatha is making a cake’, as opposed to ‘Agatha makes a cake’. This account presupposes a version of the so-called “Aristotelian” classification of verbs in English into EVENT, PROCESS and STATE verbs. The second goal of this paper is to refine this classification so as to account for the infamous “category switch” problem, the problem of how it is that modification of a verb like ‘run’ by an adverbial like ‘to the store’ can turn a PROCESS phrase (‘run’) into an EVENT phrase (‘run to the store’). Views discussed include those of Aqvist, Bach, Bennett, Bennett and Partee, Dowry, Montague and Scott, and Vendler.

Device and method for interacting with user by speech

Abstract: PROBLEM TO BE SOLVED: To provide a speech interaction device capable of easily correcting an error part without interrupting interaction. SOLUTION: The speech interaction device comprises: a candidate generation section 112 which recognizes speech, and which generates a candidate of response and a likelihood for showing probability of the candidate of response; a response sentence generation section 113 for generating a response sentence including a phrase for expressing a content that the candidate of the most likely response is selected; an output section 102 for outputting synthesis speech of response sentence; a correction phrase generation section 114 for generating at least one correction phrase corresponding to the phrase included in the response sentence by analyzing the recognition result for the speech a user utters during an output of synthesis speech; a selection section 115 which obtains the candidate of the response including the phrase of the same meaning content with the generated correction phrase from the generated candidate of the response, and which selects the candidate of the most likely response in the obtained response candidates; and an update section 116 for updating the response sentence with the phrase expressing the content of the candidate of the selected response. The output section 102 outputs synthesis speech of the response sentence after updating. COPYRIGHT: (C)2009,JPO&INPIT

Reordering constraints for phrase-based statistical machine translation

Abstract: In statistical machine translation, the generation of a translation hypothesis is computationally expensive. If arbitrary reorderings are permitted, the search problem is NP-hard. On the other hand, if we restrict the possible reorderings in an appropriate way, we obtain a polynomial-time search algorithm. We investigate different reordering constraints for phrase-based statistical machine translation, namely the IBM constraints and the ITG constraints. We present efficient dynamic programming algorithms for both constraints. We evaluate the constraints with respect to translation quality on two Japanese-English tasks. We show that the reordering constraints improve translation quality compared to an unconstrained search that permits arbitrary phrase reorderings. The ITG constraints preform best on both tasks and yield statistically significant improvements compared to the unconstrained search.

Affect analysis model: Novel rule-based approach to affect sensing from text

Abstract: In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).