What is a noun? It's easier to show than explain--and this book is brimming with examples. Author Brian Cleary and illustrator Jenya Promitsky creatively clarify the concept of nouns for young readers. Nouns are printed in color for easy identification, and the playful rhymes and illustrations combine to highlight key words.
Tài liệu "Danh từ - Nouns Collocations" là tập hợp những Collocations cụm danh từ ghép thường đánh lừa các em trong các đề thi TOIEC, mời các bạn cùng tham khảo, hy vọng các bạn sẽ đạt kết quả tốt trong bài thi sắp tới.
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.
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 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.
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...
This paper presents a new m e t h o d of analyzing Japanese noun phrases of the form N1 no 5/2. The Japanese postposition no roughly corresponds to of, but it has much broader usage. The method exploits a definition of N2 in a dictionary. For example, rugby no coach can be interpreted as a person who teaches technique in rugby. We illustrate the effectiveness of the m e t h o d by the analysis of 300 test noun phrases.
This paper 1 decdbes a computational treatment of the semantics of relational nouns. It covers relational nouns such as "sister.and "commander; and focuses especially on a particular subcategory of them, called function nouns ('speed; "distance', "rating'). Relational nouns are usually viewed as either requiring non-compositional semantic interpretation, or causing an undesirable proliferation of syntactic rules. In contrast to this, we present a treatment which is both syntactically uniform and semantically compositional.
Tài liệu Check The tenses, nouns, adjectives (-ing and –ed) giới thiệu tới các bạn những mẫu bài tập về thì, danh từ và tính từ. Thông qua việc giải những bài tập trong tài liệu này sẽ giúp các bạn nắm bắt và củng cố hơn kiến thức trong môn Tiếng Anh nói chung và thì, danh từ, tính từ nói riêng.
Determining the semantic intent of web queries not only involves identifying their semantic class, which is a primary focus of previous works, but also understanding their semantic structure. In this work, we formally deﬁne the semantic structure of noun phrase queries as comprised of intent heads and intent modiﬁers. We present methods that automatically identify these constituents as well as their semantic roles based on Markov and semi-Markov conditional random ﬁelds.
Identifying domain-dependent opinion words is a key problem in opinion mining and has been studied by several researchers. However, existing work has been focused on adjectives and to some extent verbs. Limited work has been done on nouns and noun phrases. In our work, we used the feature-based opinion mining model, and we found that in some domains nouns and noun phrases that indicate product features may also imply opinions.
There are several theories regarding what inﬂuences prominence assignment in English noun-noun compounds. We have developed corpus-driven models for automatically predicting prominence assignment in noun-noun compounds using feature sets based on two such theories: the informativeness theory and the semantic composition theory. The evaluation of the prediction models indicate that though both of these theories are relevant, they account for different types of variability in prominence assignment. ...
Flat noun phrase structure was, up until recently, the standard in annotation for the Penn Treebanks. With the recent addition of internal noun phrase annotation, dependency parsing and applications down the NLP pipeline are likely affected. Some machine translation systems, such as TectoMT, use deep syntax as a language transfer layer. It is proposed that changes to the noun phrase dependency parse will have a cascading effect down the NLP pipeline and in the end, improve machine translation output, even with a reduction in parser accuracy that the noun phrase structure might cause.
We present an algorithm for automatically disambiguating noun-noun compounds by deducing the correct semantic relation between their constituent words. This algorithm uses a corpus of 2,500 compounds annotated with WordNet senses and covering 139 different semantic relations (we make this corpus available online for researchers interested in the semantics of noun-noun compounds). The algorithm takes as input the WordNet senses for the nouns in a compound, ﬁnds all parent senses (hypernyms) of those senses, and searches the corpus for other compounds containing any pair of those senses.
The Penn Treebank does not annotate within base noun phrases (NPs), committing only to ﬂat structures that ignore the complexity of English NPs. This means that tools trained on Treebank data cannot learn the correct internal structure of NPs. This paper details the process of adding gold-standard bracketing within each noun phrase in the Penn Treebank. We then examine the consistency and reliability of our annotations. Finally, we use this resource to determine NP structure using several statistical approaches, thus demonstrating the utility of the corpus.
In this paper we present methods for improving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the dependency between conjunct lexical heads. Our changes to the baseline model result in an increase in NP coordination dependency f-score from 69.9% to 73.8%, which represents a relative reduction in f-score error of 13%. ...
The limited coverage of lexical-semantic resources is a signiﬁcant problem for NLP systems which can be alleviated by automatically classifying the unknown words. Supersense tagging assigns unknown nouns one of 26 broad semantic categories used by lexicographers to organise their manual insertion into W ORD N ET. Ciaramita and Johnson (2003) present a tagger which uses synonym set glosses as annotated training examples. We describe an unsupervised approach, based on vector-space similarity, which does not require annotated examples but signiﬁcantly outperforms their tagger. ...
In this paper, we explore the power of randomized algorithm to address the challenge of working with very large amounts of data. We apply these algorithms to generate noun similarity lists from 70 million pages. We reduce the running time from quadratic to practically linear in the number of elements to be computed.
In this paper, we study different centrality measures being used in predicting noun phrases appearing in the abstracts of scientific articles. Our experimental results show that centrality measures improve the accuracy of the prediction in terms of both precision and recall. We also found that the method of constructing Noun Phrase Network significantly influences the accuracy when using the centrality heuristics itself, but is negligible when it is used together with other text features in decision trees. ...
Knowledge of the anaphoricity of a noun phrase might be proﬁtably exploited by a coreference system to bypass the resolution of non-anaphoric noun phrases. Perhaps surprisingly, recent attempts to incorporate automatically acquired anaphoricity information into coreference systems, however, have led to the degradation in resolution performance. This paper examines several key issues in computing and using anaphoricity information to improve learning-based coreference systems. In particular, we present a new corpus-based approach to anaphoricity determination.