Adjective is a word which modifies a noun or pronoun
(called the adjective’s subject) by describing, identifying
or quantifying words.Adj are attributive when they premodify nouns: appear between
the determiner and the head of noun phrase.Adjective is a word which modifies a noun or pronoun
(called the adjective’s subject) by describing, identifying
or quantifying words.
The most powerful and the most perfect expression of thought and feeling through the
medium of oral language must be traced to the mastery of words. Nothing is better suited to
lead speakers and readers of English into an easy control of this language than the command
of the phrase that perfectly expresses the thought. Every speaker‟s aim is to be heard and
understood. A clear, crisp articulation holds an audience as by the spell of some irresistible
The goal of "500 Real English Phrases" is to teach you English phrases (not just individual English words) that you can use in many different situations. The phrases selected for "500 Real English Phrases" are typical expressions used by native speakers.
Tài liệu Comparison Structure Words and Phrases sau đây sẽ giúp các bạn hiểu rõ hơn về cấu trúc của từ và cụm từ thông qua việc so sánh sự giống và khác nhau giữa chúng. Với sự trình bày rõ ràng và kèm theo những ví dụ minh họa tài liệu sẽ giúp các bạn nắm bắt kiến thức một cách tốt hơn.
Automatic key phrase extraction is fundamental to the success of many recent digital library applications and semantic information retrieval techniques and a difficult and essential problem in Vietnamese natural language processing (NLP). In this work, we propose a novel method for key phrase extracting of Vietnamese text that exploits the Vietnamese Wikipedia as an ontology and exploits specific characteristics of the Vietnamese language for the key phrase selection stage.
Resolving polysemy and synonymy is required for high-quality information extraction. We present ConceptResolver, a component for the Never-Ending Language Learner (NELL) (Carlson et al., 2010) that handles both phenomena by identifying the latent concepts that noun phrases refer to. ConceptResolver performs both word sense induction and synonym resolution on relations extracted from text using an ontology and a small amount of labeled data. Domain knowledge (the ontology) guides concept creation by deﬁning a set of possible semantic types for concepts.
Syntactic analysis inﬂuences the way in which the source sentence is translated. Previous efforts add syntactic constraints to phrase-based translation by directly rewarding/punishing a hypothesis whenever it matches/violates source-side constituents. We present a new model that automatically learns syntactic constraints, including but not limited to constituent matching/violation, from training corpus. The model brackets a source phrase as to whether it satisﬁes the learnt syntactic constraints. The bracketed phrases are then translated as a whole unit by the decoder. ...
Hierarchical phrase-based models are attractive because they provide a consistent framework within which to characterize both local and long-distance reorderings, but they also make it dif cult to distinguish many implausible reorderings from those that are linguistically plausible. Rather than appealing to annotationdriven syntactic modeling, we address this problem by observing the in uential role of function words in determining syntactic structure, and introducing soft constraints on function word relationships as part of a standard log-linear hierarchical phrase-based model. ...
An efﬁcient decoding algorithm is a crucial element of any statistical machine translation system. Some researchers have noted certain similarities between SMT decoding and the famous Traveling Salesman Problem; in particular (Knight, 1999) has shown that any TSP instance can be mapped to a sub-case of a word-based SMT model, demonstrating NP-hardness of the decoding task. In this paper, we focus on the reverse mapping, showing that any phrase-based SMT decoding problem can be directly reformulated as a TSP.
The use of phrases in retrieval models has been proven to be helpful in the literature, but no particular research addresses the problem of discriminating phrases that are likely to degrade the retrieval performance from the ones that do not. In this paper, we present a retrieval framework that utilizes both words and phrases ﬂexibly, followed by a general learning-to-rank method for learning the potential contribution of a phrase in retrieval.
Statistical parsing of noun phrase (NP) structure has been hampered by a lack of goldstandard data. This is a signiﬁcant problem for CCGbank, where binary branching NP derivations are often incorrect, a result of the automatic conversion from the Penn Treebank.(N (N/N lung) (N (N/N cancer) (N deaths) ) )This structure is correct for most English NPs and is the best solution that doesn’t require manual reannotation. However, the resulting derivations often contain errors.
This paper presents a partial matching strategy for phrase-based statistical machine translation (PBSMT). Source phrases which do not appear in the training corpus can be translated by word substitution according to partially matched phrases. The advantage of this method is that it can alleviate the data sparseness problem if the amount of bilingual corpus is limited.
In this paper, we present a novel global reordering model that can be incorporated into standard phrase-based statistical machine translation. Unlike previous local reordering models that emphasize the reordering of adjacent phrase pairs (Tillmann and Zhang, 2005), our model explicitly models the reordering of long distances by directly estimating the parameters from the phrase alignments of bilingual training sentences.
This paper addresses the automatic classiﬁcation of semantic relations in noun phrases based on cross-linguistic evidence from a set of ﬁve Romance languages. A set of novel semantic and contextual English– Romance NP features is derived based on empirical observations on the distribution of the syntax and meaning of noun phrases on two corpora of different genre (Europarl and CLUVI). The features were employed in a Support Vector Machines algorithm which achieved an accuracy of 77.9% (Europarl) and 74.
In this paper we describe a novel data structure for phrase-based statistical machine translation which allows for the retrieval of arbitrarily long phrases while simultaneously using less memory than is required by current decoder implementations. We detail the computational complexity and average retrieval times for looking up phrase translations in our sufﬁx array-based data structure. We show how sampling can be used to reduce the retrieval time by orders of magnitude with no loss in translation quality. ...
We present several unsupervised statistical models for the prepositional phrase attachment task that approach the accuracy of the best supervised methods for this task. Our unsupervised approach uses a heuristic based on attachment proximity and trains from raw text that is annotated with only part-of-speech tags and morphological base forms, as opposed to attachment information. It is therefore less resource-intensive and more portable than previous corpus-based algorithm proposed for this task. ...
We present a method for constructing, maintaining and consulting a database of proper nouns. We describe noun phrases composed of a proper noun a n d / o r a description of a human occupation. They are formalized by finite state transducers (FST) and large coverage dictionaries and are applied to a corpus of newspapers. We take into account synonymy and hyperonymy. This first stage of our parsing procedure has a high degree of accuracy. We show how we can handle requests such as: 'Find all newspaper articles in a general corpus mentioning the French prime minister', or 'How...