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.

Combining clues for word alignment

Abstract: In this paper, a word alignment approach is presented which is based on a combination of clues. Word alignment clues indicate associations between words and phrases. They can be based on features such as frequency, part-of-speech, phrase type, and the actual wordform strings. Clues can be found by calculating similarity measures or learned from word aligned data. The clue alignment approach, which is proposed in this paper, makes it possible to combine association clues taking different kinds of linguistic information into account. It allows a dynamic tokenization into token units of varying size. The approach has been applied to an English/Swedish parallel text with promising results.

Abstractive Multi-Document Summarization via Phrase Selection and

Abstract: We propose an abstraction-based multi-document summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the sentence construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-of-the-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.

Viewing term proximity from a different perspective

Abstract: This paper extends the state-of-the-art probabilistic model BM25 to utilize term proximity from a new perspective. Most previous work only consider dependencies between pairs of terms, and regard phrases as additional independent evidence. It is difficult to estimate the importance of a phrase and its extra contribution to a relevance score, as the phrase actually overlaps with the component terms. This paper proposes a new approach. First, query terms are grouped locally into non-overlapping phrases that may contain one or more query terms. Second, these phrases are not scored independently but are instead treated as providing a context for the component query terms. The relevance contribution of a term occurrence is measured by how many query terms occur in the context phrase and how compact they are. Third, we replace term frequency by the accumulated relevance contribution. Consequently, term proximity is easily integrated into the probabilistic model. Experimental results on TREC-10 and TREC-11 collections show stable improvements in terms of average precision and significant improvements in terms of top precisions.

Method and system for providing representative phrase

Abstract: A method and system for providing a representative phrase corresponding to a real time (current time) popular keyword. The method and system may extend a representative criterion word, determined by analyzing morphemes of words in documents grouped into a cluster, and may combine the extended representative criterion word and the popular keyword, thereby providing the representative phrases. The method and system may display the popular keyword and the representative phrases on a web page, or the like.

Facilitating presentation by mobile device of additional content for a word or phrase upon utterance thereof

Abstract: A method for presenting additional content for a word that is part of a message, and that is presented by a mobile communication device, includes the steps of: presenting the message, including emphasizing one or more words for which respective additional content is available for presenting by the mobile communication device; receiving an utterance that includes an emphasized word for which additional content is available for presenting by the mobile communication device; and presenting the additional content for the emphasized word included in the utterance received by the mobile communication device. These steps are performed by the mobile communication device.