Word chain for the word mean

word chain: цепочка слов

Смотреть что такое «word chain» в других словарях:

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The Word Chain game for ESL students can be used to help build their vocabulary using a variety of themes. It can be used with larger groups or even in pairs.

Student Level: Beginner, Intermediate

Age Group: Kids, Adults

Word Chain ESL Game Preparation:

Not much pre-class preparation is required for the Word Chain game. If you want, you could think of a few different vocabulary themes based on the words in the textbook that you are using for the class, but it isn’t really necessary.

Word Chain ESL Game Guidelines:

To explain to the class how to do the Word Chain activity, first, think of a theme like “movie stars” or something simple like “colors” if it is a very basic level of students.

Let’s use the “colors” example to start for the explanation. Grab a board marker and write the word “yellow” on the board so that everyone can see.

Now, underline the last letter of the word yellow and draw an arrow to the next word that they need to think of that starts with “W”. Tell them it has to be a color word only. They should come up with “white” fairly easily.

With the last letter of “white” being “E”, draw another arrow to start the next color word beginning with “E”.

Give students some time to answer. If nobody can think of a color that begins with “E” then suggest that they could have said the color “emerald green” and carry on with “N”. This should be enough to give them the idea of what to do for the game.

Next, have the students form a circle standing up or sitting at their desks in some kind of circular arrangement.

When everyone is ready, tell them that they are going to do the same activity, but this time they have to think of the words quickly and individually one after the other taking turns. Any student who cannot think of the next word fast enough is out of the game.

Try different themes to keep it interesting and challenging for the students. The rules can be varied somewhat, but this is the general aim of the Word Chain game.

Once you have completed a number of rounds, you could award the best student with a prize for positive reinforcement, especially if they are children.

Follow-Up ESL Activities:

As an extension to the word chain game, you could give the students five words from the activity and have them write sentences using the words.

Alternatively, you could try to find movie scenes that use words from the game and try the Movie Dialog Listening Activity as an extension to the lesson.

For a fun discussion extension to the lesson, most students really enjoy the Funny or Die ESL Speaking Activity.

More ESL Vocabulary Games for Kids and Adults:

I Spy
No Harm No Vowel
To Be
Halloween
Comparatives
Prepositions
Body Parts
Toilet Paper
Have You Ever
Tic Tac Toe
Word Whack
Hangman
Hot Seat
Simon Says
Pass the Marker
Memory Race
Board Race
Mystery Word
20 Questions

View the vocabulary games archive.

View more ESL activities.

Related ESL Resources Online:

ESL Games for Kids on ESLkidsStuff.com

Источник

I’ve recently been using the word chains kata for interviewing, and I thought it might be interesting to try using Erlang, and property testing.

The first step is to get a word list. I thought most linux distros came with a dictionary file, but my laptop only had a crack lib, which wasn’t really what I was looking for.

I had used this npm package before, so I just downloaded the text file it provides. With that hand, getting a list of words is easy:

word_list() ->
    {ok, Data} = file:read_file("words.txt"),
    binary:split(Data, [<<"n">>], [global]).

The next step is to find all words that are one letter away from the first word. So we create a property:

prop_next_word_should_be_new() ->
    ?FORALL({FirstWord, LastWord}, valid_words(),
        begin
            NextWord = word_chains:next_word(FirstWord, LastWord),
            NextWord =/= FirstWord
        end).

For each first word/last word pair, we check that the next word is different from the first word. We also need a generator, of valid words:

valid_words() ->
    ?SUCHTHAT({FirstWord, LastWord},
        ?LET(N, choose(2, 10),
            begin
                WordList = word_chains:word_list(),
                SameLengthWords = lists:filter(fun(W) -> length(W) =:= N end, WordList),
                {random_word(SameLengthWords), random_word(SameLengthWords)}
            end),
    FirstWord =/= LastWord).

random_word(Words) ->
    lists:nth(rand:uniform(length(Words)), Words).

First we pick a random number, in the range (2, 10), and then pick 2 words of that length, from the full word list, at random. This could result in the same word being used as both first & last word, so we filter that out, using the ?SUCHTHAT macro.

For now, we can make this pass by simply returning the last word:

next_word(_FirstWord, LastWord) ->
    LastWord.

$ ./rebar3 proper
===> Verifying dependencies...
===> Compiling word_chains
===> Testing prop_word_chains:prop_next_word_should_be_new()
....................................................................................................
OK: Passed 100 test(s).
===> 
1/1 properties passed

Boom! Next time, a more useful implementation of next word.

Источник

For the games in which one word is progressively changed into another, see Word Ladder and Word golf.

Word chain, also known as grab on behind, last and first, alpha and omega, and the name game,^[1]^[2]^[3] is a word game in which players come up with words that begin with the letter or letters that the previous word ended with. A category of words is usually chosen, there is a time limit such as five seconds,^[1]^[4] and words may not be repeated in the same game.^[5] The version of the game in which cities are used is called geography.

TimeEdit

An example chain for food would be: Soup — Peas — Sugar — Rice.^[1]

The game is used as a tool for teaching English as a second language^[6]^[7] and as a car game.^[5]

Edit

A similar Japanese game is shiritori, in which the word must begin with the last mora, or kana, of the previous word. It includes a rule for loss: words ending with N may not be used since the kana is never used in the beginning of words. The game antakshari (ant means end, akshar means letter), played in India, Pakistan and Nepal also involves chaining, but with verses of movie songs (usually Bollywood songs). In Russia a game similar to the Word chain is called Words (Russian: слова), or «A Game of Cities» (Игра в города) if played using city and town names. In French-speaking countries, the game marabout involves the last syllable.

In Chinese languages a similar game is known as jielong (接龍), where players start new words with the last hanzi of the preceding word. Another variant of the game is known as tzuchuan (字串), which utilises adding, removing or replacing of one of the character’s components to form another character. The most popular variant of the game is known as chengyu jielong (成語接龍), which involves four character idioms instead.

There is also a similar South Slavic game called kalodont, in which players continue the chain by beginning with last two letters of the previous word.

In Korean, a similar game is kkeunmaritgi (끝말잇기), in which players must say a word that starts with the last Hangul letter of the previous word. In Romanian, there is a game called «Fazan» («Pheasant»), in which players must say a word that starts with the last two letters of the previous word.

Writing poetry following the same principle is called capping verses.^[8]^[9] Various other variants exist, such as Ancient Greek skolion.

ReferencesEdit

^ ^a ^b ^c Wise, Debra; Sandra Forrest (2003). Great big book of children’s games: over 450 indoor and outdoor games for kids. McGraw-Hill Professional. ISBN 0-07-142246-3.
^ Wood, Clemend; Gloria Goddard (1940). The complete book of games (2 ed.). Garden City.
^ «How to Play the Name Game!». The Van Gogh-Goghs. 1999. Retrieved 27 April 2019.
^ Cullen, Ruth (2004). Brainiac’s Gross-Out Activity Book. Activity Journal Series. Peter Pauper Press. ISBN 0-88088-448-7.
^ ^a ^b Rosenthal, Aaron. «Are We There Yet?». Street Directory. Retrieved 8 December 2009.
^ Sperling, Dave. «w-o-r-d c-h-a-i-n». Dave’s ESL Cafe. Retrieved 8 December 2009.
^ Hill, Monica (2005). «Fun Vocabulary Learning Activities». Harsh words: English words for Chinese learners. Hong Kong University Press. p. 138. ISBN 962-209-717-0.
^ Cap Verses (To)., Dictionary of Phrase and Fable, Brewer, 1898
^ Brewer, E. Cobham (1898). «Cap Verses (To).». Dictionary of Phrase and Fable.

Источник

From Wikipedia, the free encyclopedia

The sequence between semantic related ordered words is classified as a lexical chain.^[1] A lexical chain is a sequence of related words in writing, spanning short (adjacent words or sentences) or long distances (entire text). A chain is independent of the grammatical structure of the text and in effect it is a list of words that captures a portion of the cohesive structure of the text. A lexical chain can provide a context for the resolution of an ambiguous term and enable identification of the concept that the term represents.

Rome → capital → city → inhabitant
Wikipedia → resource → web

About[edit]

Morris and Hirst^[1] introduce the term lexical chain as an expansion of lexical cohesion.^[2] A text in which many of its sentences are semantically connected often produces a certain degree of continuity in its ideas, providing good cohesion among its sentences. The definition used for lexical cohesion states that coherence is a result of cohesion, not the other way around.^[2]^[3] Cohesion is related to a set of words that belong together because of abstract or concrete relation. Coherence, on the other hand, is concerned with the actual meaning in the whole text.^[1]

Morris and Hirst^[1] define that lexical chains make use of semantic context for interpreting words, concepts, and sentences. In contrast, lexical cohesion is more focused on the relationships of word pairs. Lexical chains extend this notion to a serial number of adjacent words. There are two main reasons why lexical chains are essential:^[1]

Feasible context to assist in the ambiguity and narrowing problems to a specific meaning of a word; and
Clues to determine coherence and discourse, thus a deeper semantic-structural meaning of the text.

The method presented by Morris and Hirst^[1] is the first to bring the concept of lexical cohesion to computer systems via lexical chains. Using their intuition, they identify lexical chains in text documents and built their structure considering Halliday and Hassan’s^[2] observations. For this task, they considered five text documents, totaling 183 sentences from different and non-specific sources. Repetitive words (e.g., high-frequency words, pronouns, propositions, verbal auxiliaries) were not considered as prospective chain elements since they do not bring much semantic value to the structure themselves.

Lexical chains are built according to a series of relationships between words in a text document. In the seminal work of Morris and Hirst^[1] they consider an external thesaurus (Roget’s Thesaurus) as their lexical database to extract these relations. A lexical chain is formed by a sequence of words ${displaystyle w_{1},w_{2},dotsc ,w_{n}}$ appearing in this order, such that any two consecutive words ${displaystyle w_{i},w_{i+1}}$ present the following properties (i.e., attributes such as category, indexes, and pointers in the lexical database):^[1]^[4]

two words share one common category in their index;
the category of one of these words points to the other word;
one of the words belongs to the other word’s entry or category;
two words are semantically related; and
their categories agree to a common category.

Approaches and Methods[edit]

The use of lexical chains in natural language processing tasks (e.g., text similarity, word sense disambiguation, document clustering) has been widely studied in the literature. Barzilay et al ^[5] use lexical chains to produce summaries from texts. They propose a technique based on four steps: segmentation of original text, construction of lexical chains, identification of reliable chains, and extraction of significant sentences. Silber and McCoy^[6] also investigates text summarization, but their approach for constructing the lexical chains runs in linear time.

Some authors use WordNet^[7]^[8] to improve the search and evaluation of lexical chains. Budanitsky and Kirst^[9]^[10] compare several measurements of semantic distance and relatedness using lexical chains in conjunction with WordNet. Their study concludes that the similarity measure of Jiang and Conrath^[11] presents the best overall result. Moldovan and Adrian^[12] study the use of lexical chains for finding topically related words for question answering systems. This is done considering the glosses for each synset in WordNet. According to their findings, topical relations via lexical chains improve the performance of question answering systems when combined with WordNet. McCarthy et al.^[13] present a methodology to categorize and find the most predominant synsets in unlabeled texts using WordNet. Different from traditional approaches (e.g., BOW), they consider relationships between terms not occurring explicitly. Ercan and Cicekli^[14] explore the effects of lexical chains in the keyword extraction task through a supervised machine learning perspective. In Wei et al.^[15] combine lexical chains and WordNet to extract a set of semantically related words from texts and use them for clustering. Their approach uses an ontological hierarchical structure to provide a more accurate assessment of similarity between terms during the word sense disambiguation task.

Lexical Chain and Word Embedding[edit]

Even though the applicability of lexical chains is diverse, there is little work exploring them with recent advances in NLP, more specifically with word embeddings. In,^[16] lexical chains are built using specific patterns found on WordNet^[7] and used for learning word embeddings. Their resulting vectors, are validated in the document similarity task. Gonzales et al. ^[17] use word-sense embeddings to produce lexical chains that are integrated with a neural machine translation model. Mascarelli^[18] proposes a model that uses lexical chains to leverage statistical machine translation by using a document encoder. Instead of using an external lexical database, they use word embeddings to detect the lexical chains in the source text.

Ruas et al.^[4] propose two techniques that combine lexical databases, lexical chains, and word embeddings, namely Flexible Lexical Chain II (FLLC II) and Fixed Lexical Chain II (FXLC II). The main goal of both FLLC II and FXLC II is to represent a collection of words by their semantic values more concisely. In FLLC II, the lexical chains are assembled dynamically according to the semantic content for each term evaluated and the relationship with its adjacent neighbors. As long as there is a semantic relation that connects two or more words, they should be combined into a unique concept. The semantic relationship is obtained through WordNet, which works a ground truth to indicate which lexical structure connects two words (e.g., hypernyms, hyponyms, meronyms). If a word without any semantic affinity with the current chain presents itself, a new lexical chain is initialized. On the other hand, FXLC II breaks text segments into pre-defined chunks, with a specific number of words each. Different from FLLC II, the FXLC II technique groups a certain amount of words into the same structure, regardless of the semantic relatedness expressed in the lexical database. In both methods, each formed chain is represented by the word whose pre-trained word embedding vector is most similar to the average vector of the constituent words in that same chain.

References[edit]

^ ^a ^b ^c ^d ^e ^f ^g ^h MorrisJane; HirstGraeme (1991-03-01). «Lexical cohesion computed by thesaural relations as an indicator of the structure of text». Computational Linguistics.
^ ^a ^b ^c Halliday, Michael Alexander Kirkwood (1976). Cohesion in English. Hasan, Ruqaiya. London: Longman. ISBN 0-582-55031-9. OCLC 2323723.
^ Carrell, Patricia L. (1982). «Cohesion Is Not Coherence». TESOL Quarterly. 16 (4): 479–488. doi:10.2307/3586466. ISSN 0039-8322. JSTOR 3586466.
^ ^a ^b Ruas, Terry; Ferreira, Charles Henrique Porto; Grosky, William; de França, Fabrício Olivetti; de Medeiros, Débora Maria Rossi (2020-09-01). «Enhanced word embeddings using multi-semantic representation through lexical chains». Information Sciences. 532: 16–32. arXiv:2101.09023. doi:10.1016/j.ins.2020.04.048. ISSN 0020-0255. S2CID 218954068.
^ Barzilay, Regina; McKeown, Kathleen R.; Elhadad, Michael (1999). «Information fusion in the context of multi-document summarization». Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics. College Park, Maryland: Association for Computational Linguistics: 550–557. doi:10.3115/1034678.1034760. ISBN 1558606092.
^ Silber, Gregory; McCoy, Kathleen (2001). «Efficient text summarization using lexical chains | Proceedings of the 5th international conference on Intelligent user interfaces»: 252–255. doi:10.1145/325737.325861. S2CID 8403554.
^ ^a ^b «WordNet | A Lexical Database for English». wordnet.princeton.edu. Retrieved 2020-05-20.
^ WordNet : an electronic lexical database. Fellbaum, Christiane. Cambridge, Mass: MIT Press. 1998. ISBN 0-262-06197-X. OCLC 38104682.{{cite book}}: CS1 maint: others (link)
^ Budanitsky, Alexander; Hirst, Graeme (2001). «Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures» (PDF). Proceedings of the Workshop on WordNet and Other Lexical Resources, Second Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL-2001). pp. 24–29. Retrieved 2020-05-20.{{cite web}}: CS1 maint: location (link) CS1 maint: url-status (link)
^ Budanitsky, Alexander; Hirst, Graeme (2006). «Evaluating WordNet-based Measures of Lexical Semantic Relatedness». Computational Linguistics. 32 (1): 13–47. doi:10.1162/coli.2006.32.1.13. ISSN 0891-2017. S2CID 838777.
^ Jiang, Jay J.; Conrath, David W. (1997-09-20). «Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy». arXiv:cmp-lg/9709008.
^ Moldovan, Dan; Novischi, Adrian (2002). «Lexical chains for question answering». Proceedings of the 19th International Conference on Computational Linguistics. Taipei, Taiwan: Association for Computational Linguistics. 1: 1–7. doi:10.3115/1072228.1072395.
^ McCarthy, Diana; Koeling, Rob; Weeds, Julie; Carroll, John (2004). «Finding predominant word senses in untagged text». Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics — ACL ’04. Barcelona, Spain: Association for Computational Linguistics: 279–es. doi:10.3115/1218955.1218991.
^ Ercan, Gonenc; Cicekli, Ilyas (2007). «Using lexical chains for keyword extraction». Information Processing & Management. 43 (6): 1705–1714. doi:10.1016/j.ipm.2007.01.015. hdl:11693/23343.
^ Wei, Tingting; Lu, Yonghe; Chang, Huiyou; Zhou, Qiang; Bao, Xianyu (2015). «A semantic approach for text clustering using WordNet and lexical chains». Expert Systems with Applications. 42 (4): 2264–2275. doi:10.1016/j.eswa.2014.10.023.
^ Linguistic Modeling and Knowledge Processing Department, Institute of Information and Communication Technology, Bulgarian Academy of Sciences; Simov, Kiril; Boytcheva, Svetla; Osenova, Petya (2017-11-10). «Towards Lexical Chains for Knowledge-Graph-basedWord Embeddings» (PDF). RANLP 2017 — Recent Advances in Natural Language Processing Meet Deep Learning. Incoma Ltd. Shoumen, Bulgaria: 679–685. doi:10.26615/978-954-452-049-6_087. ISBN 978-954-452-049-6. S2CID 41952796.{{cite journal}}: CS1 maint: multiple names: authors list (link)
^ Rios Gonzales, Annette; Mascarell, Laura; Sennrich, Rico (2017). «Improving Word Sense Disambiguation in Neural Machine Translation with Sense Embeddings». Proceedings of the Second Conference on Machine Translation. Copenhagen, Denmark: Association for Computational Linguistics: 11–19. doi:10.18653/v1/W17-4702.
^ Mascarell, Laura (2017). «Lexical Chains meet Word Embeddings in Document-level Statistical Machine Translation». Proceedings of the Third Workshop on Discourse in Machine Translation. Copenhagen, Denmark: Association for Computational Linguistics: 99–109. doi:10.18653/v1/W17-4813.

Источник

Test memory skills and on the spot thinking with Word Chains, a highly adaptable icebreaker for smaller groups. Players will sit in a circle and list words in a category. They must remember the words said previously and think of unique words to add. This icebreaker challenges memory skills and is great for players of any age. Word Chains naturally gets more difficult the longer it’s played. This means that players will be able to take the game to their skill level organically.

One variation of Word Chains is to start the game with the phrase, “I’m going on a picnic and I’m going to bring…” Each player would add an item to the list after listing everything that came before. Any category of object can be used for Word Chains, such as types of fruit, state names, football teams, colors, items of clothing, and country music singers. The game can be made more difficult for older audiences by requiring that the words are in alphabetical order, although vaguer categories should be used to allow players flexibility to find all the words.

Materials

Pencil and Paper. You may find the game more interesting if you take notes and track the phrase, or to act as mediator in case disputes arise.
List of categories or rule variations to try
30-second timer

How To Play Word Chains

Arrange the participants in a circle and explain the rules of the game, any variations, and the theme of the word chain.
Start the word chain yourself or designate someone as the start of the chain. Example: Theme – types of fruit. First word in the chain: Apple.
The second person will have to repeat the chain, and add a unique word that fits the category. Example: Apple, Watermelon.
Each person will have 30 seconds after correcting reciting the chain to add another new word.
The icebreaker can be repeated with different categories or with different rule variations to make the game more challenging.
The chain is broken when a player cannot correctly recite the chain or can’t think of a word to add to it within 30 seconds.

Tips and Notes

For a more competitive spin, don’t end the game when someone makes a mistake. Instead, that person can sit out and the game can continue until you find the champion of Word Chains.

Feel free to adapt Word Chains to your group’s level.

Asking the group to think of words in alphabetical order makes it easier to remember the word chain and more difficult to think of a new word.
Asking players to start each word with the letter that ends the word before it makes it more difficult to both remember the chain and think of a new word.
Instead of category, players could list groups of words with 3 letters, or groups of words with 2 syllables, etc.

Источник

Смотреть что такое «word chain» в других словарях:

Word Chain ESL Game Preparation:

Word Chain ESL Game Guidelines:

Follow-Up ESL Activities:

More ESL Vocabulary Games for Kids and Adults:

Related ESL Resources Online:

TimeEdit

Edit

See alsoEdit

ReferencesEdit

About[edit]

Approaches and Methods[edit]

Lexical Chain and Word Embedding[edit]

See also[edit]

References[edit]