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.

Simple Composition: A Magnetoencephalography Investigation into the Comprehension of Minimal Linguistic Phrases

Abstract: The expressive power of language lies in its ability to construct an infinite array of ideas out of a finite set of pieces. Surprisingly, few neurolinguistic investigations probe the basic processes that constitute the foundation of this ability, choosing instead to focus on relatively complex combinatorial operations. Contrastingly, in the present work, we investigate the neural circuits underlying simple linguistic composition, such as required by the minimal phrase "red boat." Using magnetoencephalography, we examined activity in humans generated at the visual presentation of target nouns, such as "boat," and varied the combinatorial operations induced by its surrounding context. Nouns in minimal compositional contexts ("red boat") were compared with those appearing in matched non-compositional contexts, such as after an unpronounceable consonant string ("xkq boat") or within a list ("cup, boat"). Source analysis did not implicate traditional language areas (inferior frontal gyrus, posterior temporal regions) in such basic composition. Instead, we found increased combinatorial-related activity in the left anterior temporal lobe (LATL) and ventromedial prefrontal cortex (vmPFC). These regions have been linked previously to syntactic (LATL) and semantic (vmPFC) combinatorial processing in more complex linguistic contexts. Thus, we suggest that these regions play a role in basic syntactic and semantic composition, respectively. Importantly, the temporal ordering of the effects, in which LATL activity (∼225 ms) precedes vmPFC activity (∼400 ms), is consistent with many processing models that posit syntactic composition before semantic composition during the construction of linguistic representations.

Discovering trends in text databases

Abstract: We address the problem of discovering trends in text databases. Trends can be used, for example, to discover that a company is shifting interests from one domain to another. We are given a database V of documents. Each document consists of one or more text fields and a timestamp. The unit of text is a word and a phrase is a list of words. (We defer the discussion of more complex structures till the “Methodology” secl-inn \ Ao.aw.;,tc.rl ..r;th r...rh nhrano ;a s h;rtmw nf the YAVU., ~uu”~Icu”n,L& ““lull \.uIUIA yuLCll”U I” Lo ,YYUY”~ y “I Yll” frequency of occurrence of the phrase, obtained by partitioning the documents based upon their timestamps. The frequency of occurrence in a particular time period is the number of documents that contain the phrase. (Other measures of frequency are possible, e.g. counting each occurrence of the phrase in a document.) A trend is a specific subsequence of the history of a phrase that satisfies the users’ query over the histories. For example, the user may specify a “spike” query to finds those phrases whose frequency of occurrence increased and then decreased.

SemEval-2007 Task 10: English Lexical Substitution Task

Abstract: In this paper we describe the English Lexical Substitution task for SemEval. In the task, annotators and systems find an alternative substitute word or phrase for a target word in context. The task involves both finding the synonyms and disambiguating the context. Participating systems are free to use any lexical resource. There is a subtask which requires identifying cases where the word is functioning as part of a multiword in the sentence and detecting what that multiword is.

Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation

Abstract: We describe a new approach to SMT adaptation that weights out-of-domain phrase pairs according to their relevance to the target domain, determined by both how similar to it they appear to be, and whether they belong to general language or not. This extends previous work on discriminative weighting by using a finer granularity, focusing on the properties of instances rather than corpus components, and using a simpler training procedure. We incorporate instance weighting into a mixture-model framework, and find that it yields consistent improvements over a wide range of baselines.

Talking video games

Abstract: This invention consists of methods of simulating voice conversations with talking animated characters (17, 18) on a television or video screen (11). The animated characters (17, 18) talk to each other and to humans (10, 12). Each human holds a light-weight controller (47, 48) that has push buttons (14) next to a display of variable phrases or sentences (13) for each human's side of the dialog. This dialog includes alternative words (13, 26) for a human player to say to a character or for a character to say (15, 16, 20, 23, 27) or actions (Figs. 3, 7, 10) for a character to do. A human (10, 12) responds to what a character (17, 18) says or does by pressing a button (14) next to a selected phrase. An animated character then vocally responds (15 in Fig. 1) to the selected phrase as if it had been spoken by a human or as if it were the character's own words. Each scene (64) branches to subsequent scenes (65, 66, 67) and within each scene there are several branching dialog sequences (60, 61, 62, 69, 63, 68).