Study of the Vietnamese language has seldom paid attention to the
characteristics and origin of Vietnamese Personal Pronouns. This possibly
stemmed from continuing debate as to the origin of the Vietnamese language
itself, and the apparent lack of a reliable theoretical framework for the
etymology of Vietnamese lexicon, apart from conventional distinction between
Sino-Vietnamese words and pure Nôm vocabulary.
Tài liệu tham khảo môn Anh với chuyên đề: Đại từ - Pronouns dành cho các bạn học sinh và quý thầy cô nhằm phục vụ cho công tác giảng dạy, học tập và ôn thi Đại học. Mời quý thầy cô và các bạn tham khảo để củng cố kiến thức và trau dồi kinh nghiệm.
Đại từ phản thân (Reflexive pronouns)
.Chúng ta đã học các loại đại từ nhân xưng (đứng làm chủ từ và túc từ), đại từ sở hữu và tính từ sở hữu, cách viết đại từ phản thân kết hợp các loại đó.
Loại đại từ này phản chiếu lại chính chủ từ của câu. Chúng ta đã học các loại đại từ nhân xưng (đứng làm chủ từ và túc từ), đại từ sở hữu và tính từ sở hữu, cách viết đại từ phản thân kết hợp các loại đó....
This paper presents a supervised pronoun anaphora resolution system based on factorial hidden Markov models (FHMMs). The basic idea is that the hidden states of FHMMs are an explicit short-term memory with an antecedent buffer containing recently described referents. Thus an observed pronoun can ﬁnd its antecedent from the hidden buffer, or in terms of a generative model, the entries in the hidden buffer generate the corresponding pronouns.
Syntactic knowledge is important for pronoun resolution. Traditionally, the syntactic information for pronoun resolution is represented in terms of features that have to be selected and deﬁned heuristically. In the paper, we propose a kernel-based method that can automatically mine the syntactic information from the parse trees for pronoun resolution. Speciﬁcally, we utilize the parse trees directly as a structured feature and apply kernel functions to this feature, as well as other normal features, to learn the resolution classiﬁer.
In this paper we focus on how to improve pronoun resolution using the statisticsbased semantic compatibility information. We investigate two unexplored issues that inﬂuence the effectiveness of such information: statistics source and learning framework. Speciﬁcally, we for the ﬁrst time propose to utilize the web and the twin-candidate model, in addition to the previous combination of the corpus and the single-candidate model, to compute and apply the semantic information. t
such as the existence and properties of its antecedents. In fact, such information has been used for pronoun resolution in many heuristicbased systems. The S-List model (Strube, 1998), for example, assumes that a co-referring candidate is a hearer-old discourse entity and is preferred to other hearer-new candidates. In the algorithms based on the centering theory (Brennan et al., 1987; Grosz et al.
In this paper, a computational approach for resolving zero-pronouns in Spanish texts is proposed. Our approach has been evaluated with partial parsing of the text and the results obtained show that these pronouns can be resolved using similar techniques that those used for pronominal anaphora. Compared to other well-known baselines on pronominal anaphora resolution, the results obtained with our approach have been consistently better than the rest.
Most traditional approaches to anaphora resolution rely heavily on linguistic and domain knowledge. One of the disadvantages of developing a knowledgebased system, however, is that it is a very labourintensive and time-consuming task. This paper presents a robust, knowledge-poor approach to resolving pronouns in technical manuals, which operates on texts pre-processed by a part-of-speech tagger.
This paper presents a pronoun resolution algorithm that adheres to the constraints and rules of Centering Theory (Grosz et al., 1995) and is an alternative to Brennan et al.'s 1987 algorithm. The advantages of this new model, the Left-Right Centering Algorithm (LRC), lie in its incremental processing of utterances and in its low computational overhead. The algorithm is compared with three other pronoun resolution methods: Hobbs' syntax-based algorithm, Strube's S-list approach, and the B F P Centering algorithm. ...
Machine Translation is a well–established ﬁeld, yet the majority of current systems translate sentences in isolation, losing valuable contextual information from previously translated sentences in the discourse. One important type of contextual information concerns who or what a coreferring pronoun corefers to (i.e., its antecedent). Languages differ signiﬁcantly in how they achieve coreference, and awareness of antecedents is important in choosing the correct pronoun.
We present an automatic approach to determining whether a pronoun in text refers to a preceding noun phrase or is instead nonreferential. We extract the surrounding textual context of the pronoun and gather, from a large corpus, the distribution of words that occur within that context. We learn to reliably classify these distributions as representing either referential or non-referential pronoun instances. Despite its simplicity, experimental results on classifying the English pronoun it show the system achieves the highest performance yet attained on this important task. i...
We present an approach to pronoun resolution based on syntactic paths. Through a simple bootstrapping procedure, we learn the likelihood of coreference between a pronoun and a candidate noun based on the path in the parse tree between the two entities. This path information enables us to handle previously challenging resolution instances, and also robustly addresses traditional syntactic coreference constraints. Highly coreferent paths also allow mining of precise probabilistic gender/number information. ...
This paper presents a novel ensemble learning approach to resolving German pronouns. Boosting, the method in question, combines the moderately accurate hypotheses of several classiﬁers to form a highly accurate one. Experiments show that this approach is superior to a single decision-tree classiﬁer. Furthermore, we present a standalone system that resolves pronouns in unannotated text by using a fully automatic sequence of preprocessing modules that mimics the manual annotation process.
We apply a decision tree based approach to pronoun resolution in spoken dialogue. Our system deals with pronouns with NPand non-NP-antecedents. We present a set of features designed for pronoun resolution in spoken dialogue and determine the most promising features. We evaluate the system on twenty Switchboard dialogues and show that it compares well to Byron’s (2002) manually tuned system.
This paper presents a corpus-based approach for deriving heuristics to locate the antecedents of relative pronouns. The technique dupficates the performance of hand-coded rules and requires human intervention only during the training phase. Because the training instances are built on parser output rather than word cooccurrences, the technique requires a small number of training examples and can be used on small to medium-sized corpora.
The qusstlun of how people resolve pronouns has the various factors combine. been of interest to language theorists for a long time because so much of what goes on when people find referents for pronouns seems to lie at the heart of comprehension.
In this paper we present a formalization of the centering approach to modeling attentional structure in discourse and use it as the basis for an algorithm to track discourse context and bind pronouns. As described in [GJW86], the process of centering attention on entities in the discourse gives rise to the intersentential transitional states of continuing, re~aining and shifting. We propose an extension to these states which handles some additional cases of multiple ambiguous pronouns.
Prepositions are a class of words that indicate relationships between nouns, pronouns and other words in a sentence. Most often they come before a noun. They never change their form, regardless of the case, gender etc. of the word they are referring to. Each definition of a preposition is followed by one or more patterns, which indicate the
word order appropriate for the definition.
The verbs in each pattern can be changed to other tenses.
Không phải ngẫu nhiên mà ngừơi ta xem Relative pronoun : WHO ,WHICH ,WHOM....là một trong " tứ trụ" trong cấu trúc câu tiếng Anh ( cùng với : câu tường thuật , chia động từ ,câu bị động ) .Hầu như trong bài văn, bài text nào cũng ít nhiều dính dáng đến nó. Do đó các em nên chú ý học kỹ cấu trúc này nhé
Thông thường khi mới học tiếng Anh chúng ta biết đến WHO ,WHICH .. như là chữ hỏi trong câu hỏi : Who do you like ? bạn thích ai ? chữ WHO...