Named Entity Recognition using an HMM-based Chunk Tagger

Named Entity Recognition using an HMM-based Chunk Tagger

GuoDong Zhou Jian Su

Laboratories for Information Technology

21 Heng Mui Keng Terrace

Singapore 119613

zhougd@https://www.wendangku.net/doc/453500079.html,.sg sujian@https://www.wendangku.net/doc/453500079.html,.sg

Abstract

This paper proposes a Hidden Markov

Model (HMM) and an HMM-based chunk

tagger, from which a named entity (NE)

recognition (NER) system is built to

recognize and classify names, times and

numerical quantities. Through the HMM,

our system is able to apply and integrate

four types of internal and external

evidences: 1) simple deterministic internal

feature of the words, such as capitalization

and digitalization; 2) internal semantic

feature of important triggers; 3) internal

gazetteer feature; 4) external macro context

feature. In this way, the NER problem can

be resolved effectively. Evaluation of our

system on MUC-6 and MUC-7 English NE

tasks achieves F-measures of 96.6% and

94.1% respectively. It shows that the

performance is significantly better than

reported by any other machine-learning

system. Moreover, the performance is even

consistently better than those based on

handcrafted rules.

1 Introduction

Named Entity (NE) Recognition (NER) is to classify every word in a document into some predefined categories and "none-of-the-above". In the taxonomy of computational linguistics tasks, it falls under the domain of "information extraction", which extracts specific kinds of information from documents as opposed to the more general task of "document management" which seeks to extract all of the information found in a document.

Since entity names form the main content of a document, NER is a very important step toward more intelligent information extraction and management. The atomic elements of information extraction -- indeed, of language as a whole -- could be considered as the "who", "where" and "how much" in a sentence. NER performs what is known as surface parsing, delimiting sequences of tokens that answer these important questions. NER can also be used as the first step in a chain of processors: a next level of processing could relate two or more NEs, or perhaps even give semantics to that relationship using a verb. In this way, further processing could discover the "what" and "how" of a sentence or body of text.

While NER is relatively simple and it is fairly easy to build a system with reasonable performance, there are still a large number of ambiguous cases that make it difficult to attain human performance. There has been a considerable amount of work on NER problem, which aims to address many of these ambiguity, robustness and portability issues. During last decade, NER has drawn more and more attention from the NE tasks [Chinchor95a] [Chinchor98a] in MUCs [MUC6] [MUC7], where person names, location names, organization names, dates, times, percentages and money amounts are to be delimited in text using SGML mark-ups.

Previous approaches have typically used manually constructed finite state patterns, which attempt to match against a sequence of words in much the same way as a general regular expression matcher. Typical systems are Univ. of Sheffield's LaSIE-II [Humphreys+98], ISOQuest's NetOwl [Aone+98] [Krupha+98] and Univ. of Edinburgh's LTG [Mikheev+98] [Mikheev+99] for English NER. These systems are mainly rule-based. However, rule-based approaches lack the ability of coping with the problems of robustness and portability. Each new source of text requires significant tweaking of rules to maintain optimal performance and the maintenance costs could be quite steep.

The current trend in NER is to use the machine-learning approach, which is more

Computational Linguistics (ACL), Philadelphia, July 2002, pp. 473-480. Proceedings of the 40th Annual Meeting of the Association for

attractive in that it is trainable and adaptable and the

maintenance of a machine-learning system is much cheaper than that of a rule-based one. The representative machine-learning approaches used in NER are HMM (BBN's IdentiFinder in [Miller+98] [Bikel+99] and KRDL's system [Yu+98] for Chinese NER.), Maximum Entropy (New York Univ.'s MEME in [Borthwick+98] [Borthwich99]) and Decision Tree (New York Univ.'s system in [Sekine98] and SRA's system in [Bennett+97]). Besides, a variant of Eric Brill's transformation-based rules [Brill95] has been applied to the problem [Aberdeen+95]. Among

these approaches, the evaluation performance of

HMM is higher than those of others. The main reason may be due to its better ability of capturing

the locality of phenomena, which indicates names in text. Moreover, HMM seems more and more used in NE recognition because of the efficiency of the Viterbi algorithm [Viterbi67] used in decoding the NE-class state sequence. However, the

performance of a machine-learning system is

always poorer than that of a rule-based one by about 2% [Chinchor95b] [Chinchor98b]. This may be because current machine-learning approaches capture important evidence behind NER problem much less effectively than human experts who

handcraft the rules, although machine-learning approaches always provide important statistical information that is not available to human experts. As defined in [McDonald96], there are two kinds of evidences that can be used in NER to solve the ambiguity, robustness and portability problems described above. The first is the internal evidence found within the word and/or word string itself while the second is the external evidence gathered from its context. In order to effectively apply and integrate internal and external evidences, we present a NER system using a HMM. The approach behind our NER system is based on the HMM-based chunk tagger in text chunking, which was ranked the best individual system [Zhou+00a] [Zhou+00b] in CoNLL'2000 [Tjong+00]. Here, a NE is regarded as a chunk, named "NE-Chunk". To date, our system has been successfully trained and applied in English NER. To our knowledge, our system outperforms any published

machine-learning systems. Moreover, our system even outperforms any published rule-based systems.

The layout of this paper is as follows. Section 2 gives a description of the HMM and its application in NER: HMM-based chunk tagger. Section 3 explains the word feature used to capture both the internal and external evidences. Section 4 describes the back-off schemes used to tackle the sparseness problem. Section 5 gives the experimental results of our system. Section 6 contains our remarks and possible extensions of the proposed work. 2 HMM-based Chunk Tagger 2.1 HMM Modeling Given a token sequence n

n g g g G L 211=, the goal

of NER is to find a stochastic optimal tag sequence

n n

t t t T L 211= that maximizes (2-1) )()(),(log )(log )|(log 1

111111n n n n n

n n G P T P G T P T P G T P ?+=

The second item in (2-1) is the mutual information between n T 1 and n

G 1. In order to simplify the computation of this item, we assume mutual information independence:

∑==n i n

i n n G t MI G T MI 1

111),(),( or (2-2) ∑=?=?n

i n i n i n n n n G P t P G t P G P T P G T P 1111111)

()(),(log )()(),(log (2-3)

Applying it to equation (2.1), we have: ∑∑==+?=n

i n i n

i i n

n n G t P t P T P G T P 111111)|(log )

(log )(log )|(log (2-4) The basic premise of this model is to consider the raw text, encountered when decoding, as though it had passed through a noisy channel, where it had been originally marked with NE tags. The job of our generative model is to directly generate the original NE tags from the output words of the noisy channel. It is obvious that our generative model is reverse to the generative model of traditional HMM 1, as used 1

In traditional HMM to maximise )|(log 11n n G T P , first we

apply Bayes' rule:

)(),()|(11111n n n n n G P G T P G T P =

and have:

in BBN's IdentiFinder, which models the original process that generates the NE-class annotated words from the original NE tags. Another

difference is that our model assumes mutual

information independence (2-2) while traditional HMM assumes conditional probability

independence (I-1). Assumption (2-2) is much looser than assumption (I-1) because assumption

(I-1) has the same effect with the sum of

assumptions (2-2) and (I-3)2. In this way, our model

can apply more context information to determine the tag of current token.

From equation (2-4), we can see that:

1) The first item can be computed by applying

chain rules. In ngram modeling, each tag is

assumed to be probabilistically dependent on the

N-1 previous tags.

2) The second item is the summation of log probabilities of all the individual tags. 3) The third item corresponds to the "lexical" component of the tagger.

We will not discuss both the first and second

items further in this paper. This paper will focus on the third item ∑

=n i n i G t P 1

1)|(log , which is the main difference between our tagger and other traditional HMM-based taggers, as used in BBN's IdentiFinder. Ideally, it can be estimated by using the forward-backward algorithm [Rabiner89] recursively for the 1st -order [Rabiner89] or 2nd -order HMMs [Watson+92]. However, an

alternative back-off modeling approach is applied

instead in this paper (more details in section 4).

2.2 HMM-based Chunk Tagger

))

(log )|((log max arg )

|(log max arg 11111n n n T

n

n T

T P T G P G T P +=

Then we assume conditional probability independence: ∏==n

i i i n n t g P T G P 1

11)|()|( (I-1)

and have: ))(log )|(log (max arg )|(log max arg 11

11n

n

i i i T n

n T

T P t g P G T P +=∑= (I-2) 2 We can obtain equation (I-2) from (2.4) by assuming

)|(log )|(log 1

i i n i t g P G t P = (I-3)

For NE-chunk tagging, we have token >=

word-feature sequence. In the meantime, NE-chunk

tag i t is structural and consists of three parts: 1) Boundary Category : BC = {0, 1, 2, 3}. Here 0

means that current word is a whole entity and 1/2/3 means that current word is at the beginning/in the middle/at the end of an entity. 2) Entity Category : EC. This is used to denote the

class of the entity name. 3) Word Feature : WF. Because of the limited number of boundary and entity categories, the word feature is added into the structural tag to represent more accurate models. Obviously, there exist some constraints between 1?i t and i t on the boundary and entity categories, as shown in Table 1, where "valid" / "invalid" means the tag sequence i

i t t 1? is valid / invalid while "valid

on" means i i t t 1?

is valid with an additional condition i i EC EC =?1. Such constraints have been

used in Viterbi decoding algorithm to ensure valid

NE chunking.

0 1 2 3

0 Valid Valid Invalid Invalid 1 Invalid Invalid Valid on Valid on 2 Invalid Invalid Valid Valid 3 Valid Valid Invalid Invalid Table 1: Constraints between 1?i t and i

t (Column:

1?i BC in 1?i t ; Row: i BC in i t ) 3 Determining Word Feature

As stated above, token is denoted as ordered pairs of

word-feature and word itself: >=

Here, the word-feature is a simple deterministic computation performed on the word and/or word string with appropriate consideration of context as looked up in the lexicon or added to the context. In our model, each word-feature consists of

several sub-features, which can be classified into internal sub-features and external sub-features. The internal sub-features are found within the word

and/or word string itself to capture internal evidence while external sub-features are derived within the context to capture external evidence.

3.1 Internal Sub-Features

Our model captures three types of internal sub-features: 1)1

f : simple deterministic internal feature of the words, such as capitalization and

digitalization; 2)2f : internal semantic feature of

important triggers; 3)3f : internal gazetteer feature.

1) 1

f is the basic sub-feature exploited in this model, as shown in Table 2 with the descendin

g order of priority. For example, in the case of non-disjoint feature classes suc

h as ContainsDigitAndAlpha and ContainsDigitAndDash, the former will take precedence. The first eleven features arise from

the need to distinguish and annotate monetary

amounts, percentages, times and dates. The rest of the features distinguish types of capitalization and all other words such as punctuation marks. In particular, the FirstWord feature arises from

the fact that if a word is capitalized and is the first word of the sentence, we have no good information as to why it is capitalized (but note that AllCaps and CapPeriod are computed before FirstWord, and take precedence.) This sub-feature is language dependent. Fortunately, the feature computation is an extremely small part of the implementation. This kind of internal sub-feature has been widely used in machine-learning systems, such as BBN's

IdendiFinder and New York Univ.'s MENE. The rationale behind this sub-feature is clear: a) capitalization gives good evidence of NEs in Roman languages; b) Numeric symbols can automatically be grouped into categories.

2) 2f is the semantic classification of important triggers, as seen in Table 3, and is unique to our system. It is based on the intuitions that important triggers are useful for NER and can be classified according to their semantics. This sub-feature applies to both single word and multiple words. This set of triggers is collected semi-automatically from the NEs and their local context of the training data.

3) Sub-feature 3f , as shown in Table 4, is the internal gazetteer feature, gathered from the look-up gazetteers: lists of names of persons, organizations, locations and other kinds of named entities. This sub-feature can be

determined by finding a match in the gazetteer of the corresponding NE type

where n (in Table 4) represents the word

number in the matched word string. In stead

of collecting gazetteer lists from training data, we collect a list of 20 public holidays in several countries, a list of 5,000 locations

from websites such as GeoHive 3, a list of 10,000 organization names from websites such as Yahoo 4 and a list of 10,000 famous people from websites such as Scope Systems 5. Gazetters have been widely used in NER systems to improve performance. 3.2 External Sub-Features

For external evidence, only one external macro context feature 4f , as shown in Table 5, is captured in our model. 4

f is about whether and how the encountered NE candidate is occurred in the list of NEs already recognized from the document, as shown in Table 5 (n is the word number in the matched NE from the recognized NE list and m is the matched word number between the word strin

g and the matched NE wit

h the corresponding NE type.). This sub-feature is unique to our system. The intuition behind this is the phenomena of name alias.

During decoding, the NEs already recognized from the document are stored in a list. When the system encounters a NE candidate, a name alias algorithm is invoked to dynamically determine its relationship with the NEs in the recognized list.

Initially, we also consider part-of-speech (POS) sub-feature. However, the experimental result is

disappointing that incorporation of POS even

decreases the performance by 2%. This may be because capitalization information of a word is submerged in the muddy of several POS tags and the performance of POS tagging is not satisfactory, especially for unknown capitalized words (since many of NEs include unknown capitalized words.). Therefore, POS is discarded.

3 https://www.wendangku.net/doc/453500079.html,/

4 https://www.wendangku.net/doc/453500079.html,/

5 https://www.wendangku.net/doc/453500079.html,/

Sub-Feature 1f Example Explanation/Intuition

Number OneDigitNum 9 Digital

year TwoDigitNum 90 Two-Digit

year FourDigitNum 1990 Four-Digit

Decade YearDecade 1990s Year

Code ContainsDigitAndAlpha A8956-67 Product ContainsDigitAndDash 09-99 Date ContainsDigitAndOneSlash 3/4 Fraction or Date ContainsDigitAndTwoSlashs 19/9/1999 DATE ContainsDigitAndComma 19,000 Money

Percentage ContainsDigitAndPeriod 1.00 Money,

OtherContainsDigit 123124 Other

Number

Organization AllCaps IBM

Initial CapPeriod M. Person

Name CapOtherPeriod St. Abbreviation CapPeriods N.Y. Abbreviation FirstWord First word of sentence No useful capitalization information

Word InitialCap Microsoft

Capitalized

Word LowerCase Will Un-capitalized Other $ All other words

Table 2: Sub-Feature1f: the Simple Deterministic Internal Feature of the Words

NE Type (No of Triggers) Sub-Feature 2f Example Explanation/Intuition PERCENT (5) SuffixPERCENT % Percentage Suffix

Prefix

MONEY (298)

PrefixMONEY $ Money

Suffix

Money

SuffixMONEY Dollars

DATE (52)

Suffix

SuffixDATE Day

Date

WeekDATE Monday

Date

Week

Date

MonthDATE July

Month

Date

Season

SeasonDATE Summer

Date

Period

PeriodDATE1 Month

PeriodDATE2 Quarter Quarter/Half of Year

EndDATE Weekend Date End

ModifierDATE Fiscal Modifier of Date

Suffix

TIME (15)

Time

SuffixTIME a.m.

Period

PeriodTime Morning

Time

Title

PERSON (179)

PrefixPERSON1 Mr. Person

Person

PrefixPERSON2 President

Designation

FirstNamePERSON Micheal Person First Name

LOC (36) SuffixLOC River Location Suffix

ORG (177) SuffixORG Ltd Organization Suffix

Others (148) Cardinal, Ordinal, etc. Six,, Sixth Cardinal and Ordinal Numbers Table 3: Sub-Feature 2f: the Semantic Classification of Important Triggers NE Type (Size of Gazetteer) Sub-Feature 3f Example

DATE (20) DATEnGn Christmas Day: DATE2G2

PERSON (10,000) PERSONnGn Bill Gates: PERSON2G2

LOC (5,000) LOCnGn Beijing: LOC1G1

ORG (10,000) ORGnGn United Nation: ORG2G2

Table 4: Sub-Feature 3f: the Internal Gazetteer Feature (G means Global gazetteer)

NE Type Sub-Feature Example

PERSON PERSONnLm Gates: PERSON2L1 ("Bill Gates" already recognized as a person name) LOC LOCnLm N.J.: LOC2L2 ("New Jersey" already recognized as a location name) ORG ORGnLm UN: ORG2L2 ("United Nation" already recognized as a org name)

Table 5: Sub-feature 4f : the External Macro Context Feature (L means Local document) 4 Back-off Modeling

Given the model in section 2 and word feature in

section 3, the main problem is how to

compute ∑=n

i n i G t P 1

1)/(. Ideally, we would have sufficient training data for every event whose conditional probability we wish to calculate.

Unfortunately, there is rarely enough training data

to compute accurate probabilities when decoding on new data, especially considering the complex word feature described above. In order to resolve the sparseness problem, two levels of back-off modeling are applied to approximate )/(1n i G t P : 1) First level back-off scheme is based on different contexts of word features and words themselves,

and n G 1 in )/(1n i G t P

is approximated in the descending order of i i i i w f f f 12??, 21++i i i i f f w f , i i i w f f 1?, 1+i i i f w f , i i i f w f 11??, 11++i i i w f f , i i i f f f 12??, 21++i i i f f f , i i w f , i i i f f f 12??, 1+i i f f and i f .

2) The second level back-off scheme is based on different combinations of the four sub-features described in section 3, and k f is approximated in the descending order of 4321k k k k f f f f , 31k k f f , 41k k f f , 21k k f f and 1k f .

5 Experimental Results

In this section, we will report the experimental

results of our system for English NER on MUC-6 and MUC-7 NE shared tasks, as shown in Table 6, and then for the impact of training data size on performance using MUC-7 training data. For each experiment, we have the MUC dry-run data as the held-out development data and the MUC formal test data as the held-out test data.

For both MUC-6 and MUC-7 NE tasks, Table 7 shows the performance of our system using MUC evaluation while Figure 1 gives the comparisons of our system with others. Here, the precision (P)

measures the number of correct NEs in the answer file over the total number of NEs in the answer file and the recall (R) measures the number of correct

NEs in the answer file over the total number of NEs

in the key file while F-measure is the weighted harmonic mean of precision and recall:

P R RP

F ++=2

2)1(ββ with 2β=1. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is consistently better than those based on handcrafted rules.

Statistics (KB) Training Data Dry Run Data Formal Test Data MUC-6 1330 121 124 MUC-7 708 156 561

and MUC-7 NE Tasks

F P R MUC-696.6 96.3 96.9 MUC-794.1 93.7 94.5 and MUC-7 NE Tasks

Composition F P R

1f f =

77.6 81.0 74.1 21f f f =

87.4 88.6 86.1 321f f f f = 89.3 90.5 88.2 421f f f f =

92.9 92.6 93.1

4321f f f f f =

94.1 93.7 94.5 Table 8: Impact of Different Sub-Features

With any learning technique, one important question is how much training data is required to achieve acceptable performance. More generally how does the performance vary as the training data size changes? The result is shown in Figure 2 for MUC-7 NE task. It shows that 200KB of training data would have given the performance of 90% while reducing to 100KB would have had a significant decrease in the performance. It also shows that our system still has some room for performance improvement. This may be because of

the complex word feature and the corresponding

sparseness problem existing in our system.

Figure 1: Comparison of our system with others

on MUC-6 and MUC-7 NE tasks

80

85909510080

85

9095

100

Recall P r e c i s i o n

Figure 2: Impact of Various Training Data on Performance

80

859095100100200300400500600700800

Training Data Size(KB)

F -m e a s

u r e

Another important question is about the effect of different sub-features. Table 8 answers the question on MUC-7 NE task:

1) Applying only 1f

gives our system the performance of 77.6%.

2) 2f

is very useful for NER and increases the performance further by 10% to 87.4%. 3) 4

f

is impressive too with another 5.5% performance improvement.

4) However, 3f contributes only further 1.2% to

the performance. This may be because information included in 3f has already been captured by 2f and 4f . Actually, the experiments show that the contribution of 3

f comes from where there is no explicit indicator information in/around the NE and there is no reference to other NEs in the macro context of the document. The NEs contributed by 3

f are always well-known ones, e.g. Microsoft, IBM and Bach (a composer), which are introduced in

texts without much helpful context. 6 Conclusion

This paper proposes a HMM in that a new generative model, based on the mutual information independence assumption (2-3) instead of the conditional probability independence assumption (I-1) after Bayes' rule, is applied. Moreover, it

shows that the HMM-based chunk tagger can effectively apply and integrate four different kinds of sub-features, ranging from internal word information to semantic information to NE

gazetteers to macro context of the document, to capture internal and external evidences for NER problem. It also shows that our NER system can reach "near human performance". To our knowledge, our NER system outperforms any

published machine-learning system and any

published rule-based system. While the experimental results have been impressive, there is still much that can be done

potentially to improve the performance. In the near feature, we would like to incorporate the following into our system:

? List of domain and application dependent person, organization and location names.

? More effective name alias algorithm.

? More effective strategy to the back-off modeling

and smoothing. References [Aberdeen+95] J. Aberdeen, D. Day, L.

Hirschman, P. Robinson and M. Vilain. MITRE:

Description of the Alembic System Used for MUC-6. MUC-6. Pages141-155. Columbia, Maryland. 1995.

[Aone+98] C. Aone, L. Halverson, T. Hampton, M. Ramos-Santacruz. SRA: Description of the IE2 System Used for MUC-7. MUC-7. Fairfax, Virginia. 1998. [Bennett+96] S.W. Bennett, C. Aone and C. Lovell. Learning to Tag Multilingual Texts Through Observation. EMNLP'1996. Pages109-116. Providence, Rhode Island. 1996. [Bikel+99] Daniel M. Bikel, Richard Schwartz and Ralph M. Weischedel. An Algorithm that Learns What's in a Name. Machine Learning (Special Issue on NLP). 1999.

[Borthwick+98] A. Borthwick, J. Sterling, E. Agichtein, R. Grishman. NYU: Description of the

MENE Named Entity System as Used in MUC-7. MUC-7. Fairfax, Virginia. 1998.

[Borthwick99] Andrew Borthwick. A Maximum Entropy Approach to Named Entity Recognition. Ph.D. Thesis. New York University. September, 1999. [Brill95] Eric Brill. Transform-based Error-Driven Learning and Natural Language Processing: A Case Study in Part-of-speech Tagging. Computational Linguistics 21(4). Pages543-565. 1995.

[Chinchor95a] Nancy Chinchor. MUC-6 Named Entity Task Definition (Version 2.1). MUC-6. Columbia, Maryland. 1995.

[Chinchor95b] Nancy Chinchor. Statistical Significance of MUC-6 Results. MUC-6. Columbia, Maryland. 1995.

[Chinchor98a] Nancy Chinchor. MUC-7 Named Entity Task Definition (Version 3.5). MUC-7. Fairfax, Virginia. 1998. [Chinchor98b] Nancy Chinchor. Statistical Significance of MUC-7 Results. MUC-7. Fairfax, Virginia. 1998.

[Humphreys+98] K. Humphreys, R. Gaizauskas, S. Azzam, C. Huyck, B. Mitchell, H. Cunningham, Y. Wilks. Univ. of Sheffield: Description of the LaSIE-II System as Used for MUC-7. MUC-7. Fairfax, Virginia. 1998. [Krupka+98] G. R. Krupka, K. Hausman. IsoQuest Inc.: Description of the NetOwl TM Extractor System as Used for MUC-7. MUC-7.

Fairfax, Virginia. 1998.

[McDonald96] D. McDonald. Internal and External Evidence in the Identification and Semantic Categorization of Proper Names. In B. Boguraev and J. Pustejovsky editors: Corpus Processing for Lexical Acquisition . Pages21-39. MIT Press. Cambridge, MA. 1996. [Miller+98] S. Miller, M. Crystal, H. Fox, L. Ramshaw, R. Schwartz, R. Stone, R. Weischedel, and the Annotation Group. BBN: Description of the SIFT System as Used for MUC-7. MUC-7. Fairfax, Virginia. 1998. [Mikheev+98] A. Mikheev, C. Grover, M. Moens. Description of the LTG System Used for MUC-7. MUC-7. Fairfax, Virginia. 1998. [Mikheev+99] A. Mikheev, M. Moens, and C.

Grover. Named entity recognition without gazeteers. EACL'1999. Pages1-8. Bergen, Norway. 1999.

[MUC6] Morgan Kaufmann Publishers, Inc. Proceedings of the Sixth Message Understanding

Conference (MUC-6). Columbia, Maryland. 1995. [MUC7] Morgan Kaufmann Publishers, Inc. Proceedings of the Seventh Message Understanding Conference (MUC-7). Fairfax, Virginia. 1998. [Rabiner89] L. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition”. IEEE 77(2). Pages257-285. 1989.

[Sekine98] Satoshi Sekine. Description of the

Japanese NE System Used for MET-2. MUC-7. Fairfax, Virginia. 1998. [Tjong+00] Erik F. Tjong Kim Sang and Sabine

Buchholz. Introduction to the CoNLL-2000 Shared Task: Chunking. CoNLL'2000. Pages127-132. Lisbon, Portugal. 11-14 Sept 2000.

[Viterbi67] A. J. Viterbi. Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. IEEE Transactions on Information Theory. IT(13). Pages260-269, April 1967. [Watson+92] B. Watson and Tsoi A Chunk.

Second Order Hidden Markov Models for Speech Recognition”. Proceeding of 4th Australian International Conference on Speech Science and Technology . Pages146-151. 1992. [Yu+98] Yu Shihong, Bai Shuanhu and Wu Paul. Description of the Kent Ridge Digital Labs System Used for MUC-7. MUC-7. Fairfax, Virginia. 1998.

[Zhou+00] Zhou GuoDong, Su Jian and Tey TongGuan. Hybrid Text Chunking. CoNLL'2000. Pages163-166. Lisbon, Portugal, 11-14 Sept 2000. [Zhou+00b] Zhou GuoDong and Su Jian, Error-driven HMM-based Chunk Tagger with Context-dependent Lexicon. EMNLP/ VLC'2000.

Hong Kong, 7-8 Oct 2000.

新编英语语法教程(第6版)练习参考答案

新编英语语法教程（第6版）第21讲练习参考答案Ex. 21A was sorry to learn… will be sad to hear… would be very surprised to receive… is happy to have found… was afraid to go… was pleased to hear… am very anxious to meet you. were delighted to receive your telegram. were sensible to stay indoors. clerk was prompt to answer the call. rule is easy to remember. are reluctant to leave this neighbourhood. house is difficult to heat. you ready to leave would be foolish to go out in this weather. is quick to see the point. is very keen to get on. are proud to have him as a friend. was rude not to answer your letter. are happy to have you with us this evening. Ex. 21B decision to resign surprised all of us. showed no inclination to leave.

新编英语语法教程

导论———语法层次 0.1 词素 1）自由词素 2）粘附词素 0.2 词 1）简单词、派生词、符合词 2）封闭词类和开放词类 0.3 词组 1）名词词组 2）动词词组 3）形容词词组 4）副词词组 5）介词词组 0.4分句 1）独立分句和从属分句 2）简单分句和复杂分句 3）主句和从句 4）限定分句、非限定性分句、无动词分句0.5 句子 1）完全句和不完全句 2）简单句、并列句、复杂句、并列复杂句 第1讲句子结构 1.1 主谓结构和句子分析 1）主语和谓语 2）句子分析 1.2 基本句型及其转换与扩大 1）基本句型 2）基本句型的转换与扩大 第2讲主谓一致（一） 2.1指导原则 1）语法一致 2）意义一致和就近原则 2.2 以-s 结尾的名词作主语的主谓一致问题1）以-s结尾的疾病名称和游戏名称 2）以-s结尾的学科名称 3）以-s结尾的地理名称 4）其他以-s结尾的名词 2.3 以集体名词作主语的主谓一致问题 1) 通常作复数的集体名词 2）通常作不可数名词的集体名词 3）既可作单数也可作复数的集体名词 4）a committee of 等+复数名词

第3讲主谓一致（二） 3.1 以并列结构作主语的主谓一致问题 1）由and/both... And 连接的并列主语 2）由or/nor/either...or 等连接的并列主语 3）主语+as much as 等 4）主语+as well as 等 3.2 以表示数量概念的名词词组作主语的主谓一直问题1）以表示确定数量的名词词组作主语 2) 以表示非确定数量的名词词组作主语 3.3 其他方面的主谓一致问题 1）以名词性分句作主语的主谓一致问题 2）以非限定分句作主语的主谓一致问题 3）关系分句中的主谓一致问题 4）分裂句中的主谓一致问题 5）存在句中的主谓一致问题 第4讲 4.1 名词分类和名词词组的句法功能 1）名词分类 2）名词词组的句法功能 4.2 名词的数 1）规则复数和不规则复数 2）集体名词、物质名词、抽象名词、专有名词的数4.3 单位词 1）一般表示个数的单位词 2）表示形状的单位词 3）表示容积的单位词 4）表示动作状态的单位词 5）表示成双、成对、成群的单位词 第5讲 5.1 名词属格的构成、意义和用法 1）名词属格的构成 2）名词属格的意义 3）名词属格的用法 5.2 独立属格和双重属格 1）独立属格 2）双重属格 第6讲限定词（一） 6.1限定词与三类名词的搭配关系 1）能与三类名词搭配的限定词 2）只能与单数名词搭配的限定词 3）只能与复数名词搭配的限定词

论地理环境对俄罗斯民族性格的影响

2001年1月第28卷　第1期 湖北大学学报(哲学社会科学版) Journal of Hubei University (Philos ophy and S ocial Science )Jan.,2001 V ol.28.N o.1 论地理环境对俄罗斯民族性格的影响 宋瑞芝,宋佳红 (湖北大学人文学院,湖北武汉430062) [摘　要]俄罗斯的自然地理环境在其民族性格和民族精神的形成中,虽不是唯一的因素,但却是一个极 为重要的特殊因素。它铸就了俄罗斯民族性格及精神的二律背反:美德与缺点;奴性与集体主义精神;极端与包容;肃穆与感伤。 [关键词]地理环境;民族性格;二律背反 [中图分类号]K 512 [文献标识码]A [文章编号]1001—4799(2001)01-0082-04 [收稿日期]1999-12-17 [作者简介]宋瑞芝(1942—　 ),女,北京人,湖北大学人文学院教授,主要从事世界历史研究。 世界各个民族的性格,都是由其地理环境与 其长期社会历史的原因铸就的。俄罗斯地理环境和历史发展的特殊性,造就了俄罗斯民族的特殊性格。俄罗斯民族的性格,既不同于东方民族,也不同于西方民族,可以说它包容着东、西方民族性格的某些特征,即有着民族性格上的二重性:既是世界上最无国家组织的民族,也是最崇尚专制国家的民族;既是最富有自由、漫游本性的民族,也是最富集体主义的民族;既是勤奋坚强的民族,也是最有依赖性和惰性的民族。这种民族性格上的矛盾性、二律背反,除了社会历史的诸种原因外,地理环境也对它的这种性格的形成产生了不小的影响。 一、空间的广延性:利弊双刃 俄罗斯人的祖先东斯拉夫人来自喀尔巴阡山以北、维斯杜拉河和第聂伯河之间的沼泽地。公元6世纪以后,东斯拉夫人分布在第聂伯河、伏尔加河上游及德维纳河和伊尔缅湖的广阔土地上,直到16世纪中叶,以莫斯科公国为核心的俄罗斯中央集权国家仍以伏尔加河以西为其主要的活动舞台。沙皇伊凡四世时期,开始向东征服西伯利亚,到17世纪中叶,不到一个世纪,俄罗斯的疆域迅速扩大到了亚洲的东部。19世纪时,俄罗斯的疆域已北达北冰洋沿岸,西至波罗的海,南到高加索、黑海、里海、中亚,东临太平洋西岸,形成一个东西跨度达15000公里,南北跨度达5000公里的 横跨欧亚大陆的世界第一大国。 辽阔的国土,给了俄罗斯民族一无际涯的空间。这种土地的无界性,给了俄罗斯民族一切美德,同时也给它带来了许多缺点。俄罗斯民族胸怀的宽广性,就来自于这巨大的空间。凡到过俄罗斯的人,都会看到俄罗斯人无论男女,均给人以高大粗壮的感觉,他们的建筑和日用品也像人一样,给人以厚重、宏大的印象。俄罗斯人的温顺与牺牲精神,也是无边的空间造就的。辽阔的大地给俄罗斯人一种安全感。他们身处俄罗斯的中心,周围都是自己的国土,相信俄罗斯大地母亲会帮助他、保护他,不会感到任何的恐惧。他们在俄罗斯妈妈面前,是那么地依赖、那么地温顺,他们像对待圣母那样地对待大地母亲,为她可以牺牲自己的一切。然而,也正是这个巨大的空间,却又造就了俄罗斯人的惰性,如满不在乎、责任心不强。俄罗斯人生活在这块广袤无垠的沃野上,有取之不尽、用之不竭的资源,人们无需积极进取、努力创造财富,也无需精打细算地过日子,可以自由自在地安排自己的生活。然而,辽阔的田地,却需要有序机构的管理,需要建立起一个强而有力的集权国家,这无疑对具有天然自由习性的俄罗斯人来说,是一种难以接受的压抑,从而又造成了俄罗斯人性格上的极其浮躁,个人的、集团的、阶级的首创精神的发展也受到限制。 俄罗斯大地母亲有无限宽广的胸怀,有流淌不尽的乳汁,可以养育和拯救所有的俄罗斯人,因 ? 28?

新编英语语法教程期末考试试卷

大学英语语法模拟试题 1. Mr. and Mrs. Burns feel more comfortable on a ship than they would be if they ______________any other way. A. would travel B. travelled C. are travelling D. have travelled 2. We______________ that Jim be there. A. hope B. wish C. expect D .ask 3. Lucy is glad she didn’t stay on the farm. She______________ bored. A. may be B. will be C. could be D. might have been 4. The dean of studies would have come to see you had it been possible, but he ______________so busy then. A. had been B. was C. were D. would be 5. They would certainly have come and helped us ______________time. A. did they have B. had they had C. had they have D would they have 6. If you were in better health, we______________ you to join in the work last week. A. would have allowed B. would allow C. should allow D. had allowed 7. She cried for her______________ lover. A. departed B. being departed C. departing D. having departed 8. ______________ in an important examination, one of the students in his class lost interest in his work A. Failing B. Failed C. Having been failed D. Having Failed 9. If it______________ tomorrow, I’ll stay at home. A. rained B. will rain C. had rained D. rains 10. “It’s getting very late.”“Yes, it’s time______________.” A. that we left B. we leave C. we’ll leave D. we have left a bus to go there, but he preferred to walk.

《新编英语语法教程》语法术语精编

《新编英语语法教程》主要章节语法术语Introduction: Grammatical Hierarchy (导论—语法层次) 2. Parts of speech (word class) 3. Phrases 词组 4. Clause 分句 5. Sentence 句子 1. Morpheme 词素 Free morpheme 自由词素 Bound morpheme 粘附词素 Allomorph 词素变体 Noun phrase Verb phrase Adjective phrase Adverb phrase Preposition phrase Conjunction

Lecture 1 Sentence Structure（L1）Sentence elements: S (subject) 主语V (predicate verb) 谓语动词 O (object) 宾语 C (complement) 补足语 A (Adverbial) 状语 1. Two ways of sentence analysis 1) SVO Sentence Clause NP VP NP Subject Predicate verb Object All the man have done their best. Sentence = Subject + Predicate (Predicate Verb + Object, Complement, Adverbial, etc.) ●句子由主语和谓语构成，进一步把谓语剖析为谓语动词、宾语、补语、状语等。 2) Subject + Predicate (= operator + predication) Sentence Clause Subject Predicate Operator Predication All the man have done their best. ●句子由主语和谓语构成，进一步把谓语剖析为操作词（operator）和述谓成分（predication）。 2. Basic clause types SVC, SV, SV A, SVO, SVOA, SVOC, SV oO Lecture 2 Subject-Verb Concord （L2-3） Guiding principles: Grammatical Concord Notional Concord Principle of Proximity 语法一致原则意义一致原则就近原则 Nominal clause Non-finite clause Relative clause Cleft sentence Existential clause 名词性分句非限定分句关系分句分裂句存在句Lecture 3 Noun and Noun Phrase（L4-5） 1. Classification of nouns

俄罗斯民族性格浅析

俄罗斯的民族性格浅析 段树伟 20050824033 （海洋学院 05食品） 我的家在黑龙江，可谓是全中国离俄罗斯最近的地方，地域决定文化，文化决定性格，我想我们这里的性格应该有一部分与俄罗斯民族相似。虽然我没去过这个过度，但我父亲在俄罗斯，从和父亲的谈话中，以及和从那里回来的人中，以及再选修课上老师的讲述，应该能看出一些他们民族的特点。俄罗斯民族性格具有两面性，有时非常懒惰，有时又非常勤奋；有时非常霸道，有时又非常恭顺；有时非常蛮横，有时又非常虔诚；有时非常暴躁，有时又耐性十足。有时正义十足，又有时很迂腐不堪。这就是我总结的他们民族性格特点。 世界上所有的民族都有固定的性格，比如人们通常认为中国人委婉含蓄、法国人浪漫多情、英国人绅士、美国人无羁，就是指这个民族的民族个性。而俄罗斯无疑是个例外。是什么原因使得俄民族的性格那么摇摆不定呢？这就要从他们的历史、地理说起了。 在地理方面俄罗斯由于地处欧亚两个大陆之间，既是欧洲又是亚洲，既是东方又是西方。俄罗斯人在面对东方的时候，强调它的西方特性，而当它面对欧洲时强调自己的东方特性。它处在东方文化和西方文化两个板块之间，始终摇摆不定于东西文化之问。这种东西兼存的文化色彩深深影响着俄罗斯民族性格的形成，造就了俄罗斯民族独特的个性。所以他们的性格里就有懒惰的一面，这和西方民族的性格是样的，而同时，他们的国土大部分在亚洲，又深受东方民族的影响所以有些方面他们又很勤劳，尤其女性，似乎更受东方的影响。俄的上层社会由于集中在欧洲部分，相对比较发达，西方性格更明显，所以就比较懒，或者说好听一些，更依赖高科技、现代话。而在远东的劳苦大众，他们是很勤劳的。 在历史方面，俄罗斯有悠久的复杂的经历，俄罗斯历史发展的摇摆性，矛盾性，仍然加深了俄罗斯精神的双重性。俄罗斯民族是一个天性自由、“不能也不愿意在自己的土地上建立秩序”的民族，由于东方文化的强行介入，使它建立了一个高度集权的封建专制国家。由于这个制度的残酷剥削与压迫和集体主义精神，俄罗斯人普遍表现出温顺、驯服的性格特征。但是，俄罗斯人又缺乏理性，经常感情用事，极易走极端。“决斗”便是这种性格特征的产物。19世纪以来有相当一批俄罗斯作家都是由于一时激动，走上了极端的道路——决斗。普希金死于决斗，莱蒙托夫、赫尔岑、屠格涅夫、托尔斯泰等都曾参与决斗或走到了决斗的边缘。叶利钦时代的激进改革政策更是俄罗斯人走极端的写照。70年前俄罗斯人曾对社会

新编英语语法教程期末考试试卷.doc

大学英语语法模似试题 1. Mr. and Mrs. Burns feel more comfortable on a ship than they would be if they any other way. A. would travel B. travelled C. are travelling D. have travelled 2. We that Jim be there. A. hope B. wish C. expect D .ask 3. Lucy is glad she didn^ t stay on the farm. She bored. A. may be B. will be C. could be D. might have been 4. The dean of studies would have come to see you had it been possible, but he so busy then. A. had been B. was C. were D. would be 5. They would certainly have come and helped us time. A. did they have C. had they have 6. If you were in better health, wc. A. would have allowed C. should allow 7. She cried for her A. departed B. being departed C. departing D. having departed _____________ in an important examination, one of the students in his class lost interest in his work A. Failing B. Failed C. Having been failed D. Having Failed 9. Tf it _____________ tomorrow, P 11 stay at home. A. rained B. will rain C. had rained D. rains 10. “Tt’ s getting very late. n “Yes, it’ s time __________________________ . ” A. that we left B. we leave C. we^ 11 leave D. we have 1 eft 1 Ule ______________ a bus to go there, but he preferred to walk. A. should have taken B. could take C. could have taken IX hadn^ t taken 12. _____________ the English examination I would have gone to the concert last Sunday- A. Tn spite of B. But for C. Because of D. As for 13- _____________ , we can hardly get to the station by sixclock. A. As it wi 11 be B. As it seemed C. As it is D. As if it seems 14. We hung out a lantern lest he ___________________ lost in the mist. A. gets B. get C. will get D. got 15- Tt is quite natural that such fears _______________________ . A. rise B. should arise C. should rise D. are arisen 16. Pm sorry to _________________ your private thoughts, but T think we should get on with some work. A. break in B. break on C. break in on D. break out 17. Do you think he will_________________ a cook wearing that hat? pass for B> pass as C. pass through D. pass on B. had they had D would they have ___________ you to join in the work last week. B. would allow D. had allowed 1 over. 8.

俄罗斯民族性格分析

俄罗斯民族是世界上伟大而优秀地名族之一，极富爆发力和创造力.在人类历史长河中，俄罗斯民族涌现出了无数地政治家、军事家、科学家和文学家， 为人类文明进步事业作出了卓越地贡献，赢得了世界人民地爱戴和尊敬. 为什么俄罗斯民族能够取得如此荣耀和光辉地成就呢？我认为，这与其民族性格密切相关.那么俄罗斯民族性格有哪些特点呢？我认为主要有以下几点：文档来自于网络搜索 一是具有大无畏地气概.俄罗斯民族历来傲视全球，蔑视强敌，曾经战胜了许多称霸一时、不可一世地强敌.在第二次世界大战中，俄罗斯人民以巨大地民族牺牲，战胜了当时世界上最强大地敌人德国法西斯，横扫欧洲，解放了个国家，同时派出百万大军，以雷霆万钧、摧枯拉朽之势，用十余天地时间消灭了盘据在中国东北地百万日本精锐部队关东军.苏联解体以后，面对以超级大国美国为首地西方国家地弱俄削俄与围堵政策，俄罗斯人民无所畏惧，针锋相对，强势崛起，巍然屹立在世界民族之林.文档来自于网络搜索 二是具有称雄世界地雄心壮志.享有盛誉地普京总统曾指出：“争强好胜是俄罗斯人民地民族性格.”俄罗斯人民曾经创造了无数个世界第一，赢得了世界人民地赞誉，相信俄罗斯人民还会创造出无数更加骄人地业绩.文档来自于网络搜索 三是具有坚韧不拔地意志.俄罗斯人民经历过无数艰难险阻，但事实证明，任何困难都吓不倒他们，任何凶恶地敌人也无所畏惧，他们总是愈战愈勇，愈挫愈强，显示出俄罗斯民族无比强大地爆发力、创造力、凝聚力和生命力.文档来自于网络搜索 以上几点在俄罗斯地历史中体现得淋漓尽致.比如：当年，彼得大帝为了打开通向欧洲地出海口，与瑞典人打了二十年地战争，击败了瑞典几十万大军，建立了新首都——圣彼得堡；年，拿破仑率六十万大军入侵俄罗斯，结果一败涂地，被赶出俄国，损失了大部分军队，导致后来地垮台.拿破仑说：他从未遇到过如此顽强地军队；在伟大地卫国战争中，列宁格勒围困个日日夜夜、莫斯科保卫战和斯大林格勒保卫战，苏联人民地不屈不挠，顽强战斗地精神让全世界折服.历史上，俄罗斯曾是军事强国，彼得大帝、苏沃洛夫、库图佐夫、朱可夫等世界著名军事统帅至今在俄罗斯脍炙人口.俄罗斯人崇敬英雄，现在年轻人举行婚礼地内容之一，就是到当地一个英雄地纪念碑前献花.文档来自于网络搜索 总之，俄罗斯人在国家存亡和发展地重要关头，都能毫不犹豫地挺身而出，表现出大无畏地精神.俄罗斯民族具有很强地凝聚力，不易被其他民族所压垮，却要压倒敌人.文档来自于网络搜索 我认为俄罗斯整体民族性格地形成与他地气候条件、地理位置等密切相关. 俄罗斯幅员辽阔，气候寒冷.这种自然条件促进了俄罗斯民族既奔放又忧郁，既懒散又吃苦耐劳性格特征地形成.俄罗斯有着广阔而肥沃地土地，广大地空间造就了俄罗斯民族豪放地性格.他们嗜酒无度，缺乏纪律；舞蹈奔放粗犷，合唱震天动地.但是俄罗斯漫长而寒冷地冬季却给人们留下了生活地重负与精神地压抑.因此，俄罗斯人总是表情庄严，肃穆，凝重多于微笑；心情忧郁，伤感，沉重多于轻松.此外，肥沃地土地也养成了俄罗斯人懒散地习性.他们有着取之不尽、用之不竭地资源，无须勤劳节俭去创造财富.然而恶劣地气候又使俄罗斯人饱尝了生活地艰辛，从而磨练了他们地意志，培养了他们吃苦耐劳地品格.文档来自于网络搜索 俄罗斯地跨亚欧大陆，其独特地地理位置在某种程度上创造了俄罗斯文化地民族性，同时也在某种程度上塑造了俄罗斯精神地双重性.恰达耶夫在《俄罗斯思想文集?箴言集》中说到：“有一个事实，它凌驾在我们地历史运动之上，它像一根红线贯穿着我们全部地历史…… 它是我们政治伟大之重要地因素和我们精神软弱之真正地原因，这一事实就是地理地事实.” 地跨欧亚大陆不仅仅是俄罗斯在地理位置上地特点，更是它在历史发展过程中地特点.俄罗斯民族地历史是一部不同于西方国家也不同于东方国家，却深深地受着东西方文化影响地历史.恰达耶夫说：“我们处在世界地两大部分——东方和西方之间.”“东方与西方两股历史

新编英语语法教程(第6版)第10讲练习参考答案

新编英语语法教程（第 6 版）第 10 讲练习参考答案 Ex. 10A When it comes to making a conscious effort to help keep a public place clean, most people just don ’ t make the effort. I ’ m a maintenance man for a department store. If people did make the effort, I probably wouldn ’ t haveob. a j The area that I have to spend the most time cleaning is the employees ’lunchroom . Employees go there during breaks, lunch, and dinner. The maintenance department supplies containers for garbage and ashtrays for cigaret te butts. But when they finish their food the employees will either throw their papers on the floor or leave them on the table. Some employees will on occasion throw their papers in the garbage container, but most of them who smoke will eithe r flick their ashes on the floor or in the half-filled soda cups. Cigarette butts are found anywhere other than in the ashtray, because the ashtrays may have been stolen or have been filled with gum. Sometimes an employee will remark, “ Aren ’ t these people pigs? They don ’ t even up after themselves,” as they proceed to walk away fromtheir littered table. Ex. 10B 1. its 2. his, he 3. them 4. it has 5. it, it has to 6. its / their 7. its8. him / them 9. he is / they are 10. it 11. it 12. his / their 13. isn’ t it14. take / takes 15. his / their 16. has, her 17. their 18. has, his 19. they, themselves 20. tends, itself Ex. 10C 1. it / she 2. It 3. it / her 4. her 5. his / one ’ s, he / one, his / one ’ s

中俄民族的性格差异及影响

摘要：当今社会随着全球化的发展，各国家、民族之间的政治、文化性格方面的差异也在不断地缩小。尽管如此，但有些民族所固有的性格是不会随时代的变迁而随意改变的，本文主要从中俄两国的民族性格出发，研究中俄民族的性格差异，并深入探析差异形成的原因及影响，寻找两民族性格间的契合点，希望本文的内容和观点能够对中俄文化的研究有所助益。 关键词：中国；俄罗斯；民族性格；民族性格差异 一、中俄两民族的性格特点 民族是指人们在历史上形成的一个有共同语言、共同地域、共同经济生活、共同心理素质和共同文化传统的稳定的成员共同体。相对的，民族精神就应该是各民族文化中内在的、稳定发展的一种历史传统。本部分主要分别论述中俄两民族的性格特点。以两国自身的性格作为出发点，做为基础探讨中俄两国性格上的差异及由此产生的影响。 （一）中国民族性格中的主要特点 1.中国的民族性格中“中庸”的特点 “中庸”是儒家的思想主张，至今已经有两千多年的历史了。“中庸”思想主张待人接物采取“不偏不倚、调和折中”的态度。中庸之道的理论基础是天人合一，也就是要求人道与天道相吻合。《中庸》说：“诚者，天之道也。诚之者，人之道也。诚者，不勉而中，不思而得，从容中道，圣人也。诚之者，择善而固执之者也。” 两千多年来，中国人一直奉行着儒家的“中庸之道”，凡事没有绝对，不强求也不将就，是很多中国人喜欢的处世态度。所以在中国，人们的思想绝不是极端的，人们更喜欢介于黑和白之间的灰色区域，做事讲求分寸、做人宽厚圆润。可以说在“中庸之道”的影响下，逐渐形成了中国人宽厚、圆融的性格。其实，用一种平和的心态来认识问题、解决问题，既能不伤及他人与自己的颜面，又能达到解决问题的效果，何乐而不为呢。这种“中庸之道”的性格特点在很长时间内一直影响着中国人，无论是在个人修养上，或是在与他人的交往。

东正教与俄罗斯民族性格_武玉明

第21卷第1期潍坊教育学院学报V ol.21N o.1 2008年3月JOURNA L OF WEIFANG E DUCATIONA L CO LLEGE M ar.2008 东正教与俄罗斯民族性格 武玉明 (潍坊学院外国语学院,山东潍坊　261061) 摘要:东正教是俄罗斯精神的基础和核心。在长期的历史发展中,东正教对俄罗斯民族性格产生了质的影响,俄罗斯性格的极端性、矛盾性、神秘性都可以在东正教中找到源头。宗教性是俄罗斯民族性格的本质特征。 关键词:东正教;俄罗斯民族性格;宗教性 中图分类号:D751126文献标识码:A文章编号:1009-2080(2008)01-0053-03 俄罗斯是一个具有悠久宗教传统的国家。从10世纪末到上世纪初近千年的时间里,东正教作为俄罗斯的国教,对俄罗斯国家社会生活和民族意识都曾产生过重大影响。这种影响沉淀于俄罗斯精神的核心,并在其民族性格中凸显出来。对后者的研究,多年来学术界和思想界一直是仁者见仁,智者见智,未有定论。作为俄罗斯精神文化构成的核心,东正教对俄罗斯民族性格形成的影响是不容否认的。从发生学的意义上讲,从宗教的角度来解读俄罗斯民族性格,不失为探本求源的研究途径。 一、俄罗斯东正教概述 东正教又称为正教(православие),是在东罗马帝国发展起来的基督教的一个分支。公元330年,君士坦丁一世因避乱迁都拜占庭(即君士坦丁堡),成立牧首区,视为东部教会中心,与西部教会对峙。东部教会自认为是传承正统教义的教会,是“正宗天主教”。东正教的名称即由此而来。 俄罗斯直到10世纪末才接受东正教为国教。在此之前,古俄罗斯人信奉的是多神教,即崇拜多个神祗的宗教。多神教地域性和迷信色彩很强,没有统一的教义和宗教伦理,而只是对自然力量、部落祖先和神灵的简单膜拜。除雷神佩伦、火神斯瓦罗格等这些“中心神”之外,几乎每个部落和村社都有专属自己的神。随着社会生产关系的发展,多神教越来越显示出它的弊端。基辅罗斯建立以后,为了巩固自己的统治,弗拉基米于公元988年与拜占庭帝国联姻并宣布基督教为国教,下令破除多神教的神像,强令基辅的国民到第聂伯河受洗。这就是著名的“罗斯受洗(крещениеРуси)”。 古俄罗斯的统治者接受东正教,主要是因为他们急需一神宗教来统一民族信仰和思想,以使人民相信他们的神圣地位。俄罗斯东正教从一开始就和国家政权结合在一起,并在此后的漫长历史进程中不断得到加强。东正教作为官方思想逐渐渗透到国家的政治、经济、文化之中,对社会生活的各个层面产生了深远的影响。在这个过程中,俄罗斯“民族性”和“国家性”的意识逐渐萌芽、膨胀,东正教思想从而顺利进入到俄罗斯的精神构成和伦理道德之中。 虽然东正教的引进顺应了历史潮流,是历史发展的必然趋势,但俄罗斯的东正教化并不是一帆风顺的。作为国民主体的农民本能地维护自己的神祗,但又迫于政府压力,从表面上接受了外来的“上帝”。此后,东正教与多神教进行了几个世纪的斗争和磨合,并在这个过程中不断吸收俄罗斯传统文化和多神教的一些因素,形成了具有俄罗斯特色的新的东正教文化。这种传统文化对外来文化的反影响在很大程度上改造了东正教。可以说,俄罗斯东正教化的过程也是东正教俄罗斯化的过程。时至今日,在俄罗斯宗教信仰中,我们还可以看到原始多神教的成分。 现代意义上的俄罗斯民族是在接受东正教之后才出现的。统一的东正教信仰奠定了民族意识的基础,成为俄罗斯民族身份认同的首要标志之一。同时我们也应该看到,东正教对俄罗斯的影响不只是在信仰方面,以其为中心的拜占庭文化对俄罗斯精神的影响也是巨大的。正是基于这种宗教信仰和民族精神的一致性,我们才有可能在宗教的层面上剖析谜一般的俄罗斯民族性格。 二、东正教决定了俄罗斯民族性格的本质属性 如上所述,东正教提供了俄罗斯民族意识产生和发展的深层次的平台,并进而构建了宗教色彩浓厚的俄罗斯精神的基础。它对俄罗斯民族性格的形成起到了不可替代的构成性作用。总的来看,东正教对俄罗斯民族性格的影响主要表现在以下几个方面: 1.东正教的社会历史地位决定了俄罗斯民族性格的宗教性。苏联以前的俄罗斯是政教合一的国家。东正教向人们灌输“君权神授”的思想,认为沙皇是上帝在尘世的代表,世界上只有一个上帝和一个沙皇。这种宣传得到了统治者的认同和欢迎,因而东正教的国教地位在沙俄时期一直稳如泰山。统治者从拜占庭输入东正教的本意是为了神化自己的政权, 收稿日期:2007-10-08 作者简介:武玉明(1974-),男(汉族),山东沂水人,潍坊学院外国语学院讲师,硕士。 35

地理环境对俄罗斯人性格的影响

论地理环境对俄罗斯民族性格的影响 [摘要] 俄罗斯的自然地理环境在其民族性格和民族精神的形成中,虽不是唯一的因素,但却是一个极为重要的特殊因素。它铸就了俄罗斯民族性格及精神的二律背反:美德与缺点;奴性与集体主义精神;极端与包容;肃穆与感伤。 世界各个民族的性格,都是由其地理环境与其长期社会历史的原因铸就的。俄罗斯地理环境和历史发展的特殊性,造就了俄罗斯民族的特殊性格。俄罗斯民族的性格,既不同于东方民族,也不同于西方民族,可以说它包容着东、西方民族性格的某些特征,即有着民族性格上的二重性:既是世界上最无国家组织的民族,也是最崇尚专制国家的民族;既是最富有自由、漫游本性的民族,也是最富集体主义的民族;既是勤奋坚强的民族,也是最有依赖性和惰性的民族。这种民族性格上的矛盾性、二律背反,除了社会历史的诸种原因外,地理环境也对它的这种性格的形成产生了不小的影响。 一、空间的广延性:利弊双刃 俄罗斯人的祖先东斯拉夫人来自喀尔巴阡山以北、维斯杜拉河和第聂伯河之间的沼泽地。公元6 世纪以后,东斯拉夫人分布在第聂伯河、伏尔加河上游及德维纳河和伊尔缅湖的广阔土地上,直到16 世纪中叶,以莫斯科公国为核心的俄罗斯中央集权国家仍以伏尔加河以西为其主要的活动舞台。沙皇伊凡四世时期,开始向东征服西伯利亚,到17 世纪中叶,不到一个世纪,俄罗斯的疆域迅速扩大到了亚洲的东部。19 世纪时,俄罗斯的疆域已北达北冰洋沿岸,西至波罗的海,南到高加索、黑海、里海、中亚,东临太平洋西岸,形成一个东西跨度达15000 公里,南北跨度达5000 公里的横跨欧亚大陆的世界第一大国。辽阔的国土,给了俄罗斯民族一无际涯的空间。这种土地的无界性,给了俄罗斯民族一切美德,同时也给它带来了许多缺点。俄罗斯民族胸怀的宽广性,就来自于这巨大的空间。凡到过俄罗斯的人,都会看到俄罗斯人无论男女,均给人以高大粗壮的感觉,他们的建筑和日用品也像人一样,给人以厚重、宏大的印象。俄罗斯人的温顺与牺牲精神,也是无边的空间造就的。辽阔的大地给俄罗斯人一种安全感。他们身处俄罗斯的中心,周围都是自己的国土,相信俄罗斯大地母亲会帮助他、保护他,不会感到任何的恐惧。他们在俄罗斯妈妈面前,是那么地依赖、那么地温顺,他们像对待圣母那样地对待大地母亲,为她可以牺牲自己的一切。然而,也正是这个巨大的空间,却又造就了俄罗斯人的惰性,如满不在乎、责任心不强。俄罗斯人生活在这块广袤无垠的沃野上,有取之不尽、用之不竭的资源,人们无需积极进取、努力创造财富,也无需精打细算地过日子,可以自由自在地安排自己的生活。然而,辽阔的田地,却需要有序机构的管理,需要建立起一个强而有力的集权国家,这无疑对具有天然自由习性的俄罗斯人来说,是一种难以接受的压抑,从而又造成了俄罗斯人性格上的极其浮躁,个人的、集团的、阶级的首创精神的发展也受到限制。俄罗斯大地母亲有无限宽广的胸怀,有流淌不尽的乳汁,可以养育和拯救所有的俄罗斯人,因此,俄罗斯人并不把自己看成是大地的主人、大地的统治者。相反,他们认为,俄罗斯大地是赐予他们一切的圣母,是施舍给他们一切的主人,俄罗斯人葡伏和依偎在大地母亲的怀抱中。由于这种被颠倒了的人与自然的关系,俄罗斯人总把自己置于一种被动的位置上,难以发挥自己的主动性,难以激发对社会、对生活、对祖国、对民族命运的责任感,一味听凭大地主宰。同时,俄罗斯人还得面对着现实,承受着一切压在肩上的重负(尽管是被动地承受) ,如国家的租赋,农奴主的奢侈生活,以及一切政治文化的、精神的束缚,经常处于极端穷困的、过度紧张的

从俄罗斯饮食文化浅谈民族性格

从俄罗斯饮食文化浅谈民族性格 摘要：饮食是人类生存与发展的基础, 不同的文化背景, 有着不同的饮食观念和饮食习俗，最终形成不同的饮食文化。俄罗斯民族豪迈奔放和朴实简单的民族个性深深地影响着饮食文化的发展，饮食文化的特色又充分体现了俄罗斯的民族特性。 关键词：饮食酒文化茶文化民族性格 作为亚洲板块和欧洲板块上领土面积最大的国家，虽然绝大部分面积在亚洲板块上，但是由于历史的起源发展变化，俄罗斯人最早的生息活动主要集中在欧洲地区，所以其饮食文化也就更受欧洲饮食文化影响。伴随着俄罗斯民族历史的发展,民族生活习惯的不断演变,各种饮食原料的不断丰富,并在很多方面吸取其他国家和地区的饮食文化,特别是法国菜肴的长处,俄国逐渐形成了极富有俄罗斯民族特色的饮食文化。 一、食 国家的饮食习惯大多都和该国的民族习惯以及生活方式有着直接的关系。俄罗斯人生性豪放、朴实，这也深深的影响到了他们的饮食习惯。俄罗斯人的饮食比较简单量足，喜欢吃冷菜，在食物的烹调方式上比较喜欢炸、烤、煎。由于俄罗斯的国土大部分处于纬度较高的地区，冬季漫长而寒冷。对于户外工作的人来说为了能够保证身体具有重组的热量来维持正常的活动，因此俄罗斯传统的菜肴油脂较多，往往在正规的三餐之间加餐。虽然现代俄罗斯人的生活环境大大改善，在寒冷的冬季人们大多在温暖的室内进行工作，但是这个加餐的习惯仍然被延续下来。现代俄罗斯人工作比较繁忙，人们往往根据工作的情况来调整用餐的时间。早饭比较简单，一般是面包加黄油或者是香肠，午餐一般主食为黑白面包，配菜为火腿、沙拉以及酸黄瓜等，在饭后有喝茶或者是吃甜点的习惯。俄罗斯人的晚餐比较丰富，一般有汤多为菜汤比如较为传统的罗宋汤和白菜汤，或者是肉汤，主菜多为各种肉类以及蛋类，一般还有水果。总的来说，俄罗斯人的一日三餐相对比较简单，更多的是注重量足，而我们所说的俄式大餐一般只有在正式场合，或者是重要的节日上才有。简单的饮食，浓厚的菜肴口味，更是反映了俄罗斯民族豪迈奔放和朴实的性格。 二、酒 饭可以不吃，酒不能不喝，这是俄罗斯人的一个显著特点。伏特加几乎已成为俄罗斯的另一个名称，是俄罗斯人和俄罗斯文化的重要标志。俄罗斯是横跨欧亚两大洲，领土面积居世界第一的国家。这个在世界上曾占有举足轻重地位的国家与“酒”有着密不可分的联系。伏特加的含义是“生命之水”，可见俄罗斯人是如何挚爱伏特加。 俄罗斯人爱酒其实由来已久。位于高纬度和中纬度，俄罗斯气候复杂且差异很大，地处三个气候带。夏季时，平均气温0摄氏度，冬季平均气温零下40摄氏度。全年都处于寒冷的状态，寒风呼啸，冰天雪地。可见，俄罗斯民族自形成之日起就生活在北半球最寒冷的环境当中，很难想象没有伏特加的日子是怎么样的。除此以外，历史上的俄罗斯经历着一次又一次暴虐的独裁政权。伊凡四世史称“伊凡雷帝”，他是俄罗斯历史上第一位沙皇。伊凡四世性情残忍暴力，对人民实行的统治，曾亲率特辖军讨伐诺夫哥罗德，在该城进行腥屠杀。彼得一世，是俄罗斯帝国的第一代皇帝，为了改变俄国落后的状况，对内进行了改革，同时对外扩张领土，在俄罗斯史上占有重要地位。俄罗斯人忍受着一个又一个暴虐的独裁政权，在有钱的情况下把自己灌醉是唯一的乐趣。由此可见，酒对于当时遭受压迫的俄罗斯人民来说是何其珍贵，也成为了他们活下去的精神支柱。 作为国酒，伏特加酒，更是俄罗斯人的骄傲。500年的伏特加史就是俄罗斯历史的见证。伴随着俄罗斯人经历了东欧和亚洲君主的铁蹄，沙俄的统治、十月革命、卫国战争以及苏联

浅析俄罗斯的民族性格

学号：0100000本科学年论文 浅析俄罗斯的民族性格 所在系俄罗斯语言与文学系 专业名称俄语 年级10 级 学生姓名王同学 指导老师王老师 2013年9月2日

浅析俄罗斯的民族性格 Shallow chromatography Russia’s national character Author:Wang ## Tutor: Wang## 摘要：俄罗斯的民族性格是以“面包和盐”待客的如火热情，是非黑即白、非爱即恨的极端主义，是以东正教为国教，满怀“救世主”意识的宗教主义，是一面向东一面向西的双头鹰……是众多复杂优劣性情的融合体。在该民族性格形成的过程中，有多种因素参与了它的形成过程。其中最重要的有地理环境因素和宗教因素。俄罗斯独特的地理位置、恶劣的气候环境、丰富的自然资源，既造就了俄罗斯民族的一切优点，同时也使它滋生了很多缺点。在公元10世纪到上世纪近千年的东正教俄罗斯化进程中，俄罗斯也在被东正教化，东正教的教义被俄罗斯人奉为世界观，对俄罗斯国家社会生活和民族意识产生了巨大的影响，并且成了俄罗斯精神文化构成的核心。分析俄罗斯的民族性格，有助于在当今世界经济日益全球化形势下，增进对俄罗斯的了解，增强中俄两国人民的跨文化交际意识，有效减少我们对俄罗斯这个我们千年的历史进程中的北方邻居的文化误解和冲突，对我国人民能够与俄罗斯这一邻居进行进行更有效的交流与合作具有重要意义。 关键词：俄罗斯，民族性格，地域因素，宗教因素 Abstract:Russia's national character is "bread and salt" guests like fire enthusiasm, black and white, either love or hate extremism, is based on the orthodox church of England, with "savior" of religious consciousness, is a side to east west oriented double-headed eagle...Is a complex character.In the process of formed the national character, there are many factors involved in the formation process of it.One of the most important is the geographical environment and religious factors. Russia's unique geographical location, extreme climate conditions, rich natural resources, has produced all the advantages of the russians, both at the same time also make it spawned many shortcomings.In the 10th century to the 20th century nearly one thousand years of orthodox Russia in the process of Russia is in the orthodox