| { | |
| "paper_id": "C94-1033", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T12:49:44.159537Z" | |
| }, | |
| "title": "Backtracking-Free Dictionary Access Method for Japanese Morphological Analysis", | |
| "authors": [ | |
| { | |
| "first": "Hiroshi", | |
| "middle": [], | |
| "last": "Maruyama", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Tokyo Research Laboratory", | |
| "institution": "IBM Research", | |
| "location": {} | |
| }, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "", | |
| "pdf_parse": { | |
| "paper_id": "C94-1033", | |
| "_pdf_hash": "", | |
| "abstract": [], | |
| "body_text": [ | |
| { | |
| "text": "Input sentence: ~-[-Ill:~f\u00a2:,~ e) I-3f-~: j~. --~/:. o JMA output:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Since the Japanese language does not have explicit word boundaries, dictionary lookup should be done, in principle, for all possible substrings in an input sentence. Thus, Japanese morphological analysis involves a large number of dictionary accesses.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "The standard technique for handling this problem is to use the TRIE structure to find all the words that begin at a given position in a sentence (Morimoto and Aoe 1993) . This process is executed for every character position in the sentence; that is, after looking up all the words beginning at position n, the program looks up all the words beginning at position n + 1, and so on. Therefore, some characters may be scanned more than once for different starting positions. This paper describes an attempt to minimize this 'backtracking' by using an idea similar to one proposed by Aho and Corasick (Aho 1990) for multiple-keyword string matching. When used with a 70,491-word dictionary that we developed for Japanese morphological analysis, our method reduced the number of dictionary accesses by", | |
| "cite_spans": [ | |
| { | |
| "start": 145, | |
| "end": 168, | |
| "text": "(Morimoto and Aoe 1993)", | |
| "ref_id": "BIBREF4" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "The next section briefly describes the problem and our basic idea for handling it. The detailed algorithm is given in Section 3 and Section 4, followed by the results of an experiment in Section 5. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "25%.", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "A Japanese morphological analyzer (hereafter called the JMA) takes an input sentence and segments it into words and phrases, attaching a part-of-speech code to each word at the same time. Figure 1 shows a sample input and the output of our JMA.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 188, | |
| "end": 196, | |
| "text": "Figure 1", | |
| "ref_id": "FIGREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Grammar", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Tile grammaticality of a sequence of Japanese words is mainly determined by looking at two consecutive words at a time (that is, hy looking at two-word windows). Therefore, Japanese morphological analysis is norreally done by using a Regular Grammar (e.g., Maruyama and Ogino 1994) . Our JMA grammar rules have the following general form:", | |
| "cite_spans": [ | |
| { | |
| "start": 257, | |
| "end": 281, | |
| "text": "Maruyama and Ogino 1994)", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Grammar", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "state1 ~ \"word\" [linguistic-features] state2 cost=cost.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Grammar", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Each grammar rule has a heuristic cost, and tile parse with the minimum cost will be selected as the most plausible morphological reading of the input sentence. A part of our actual gram-mar is shown in Figure 2 . Currently our grammar has about 4,300 rules and 400 nonterminal symbols.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 203, | |
| "end": 211, | |
| "text": "Figure 2", | |
| "ref_id": "FIGREF7" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Grammar", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "While the flmction words (particles, auxiliary verbs, and so on, totaling several hundred) are encoded in the grammar rules, the content words (nouns, verbs, and so on) are stored in st separate dictionary. Since content words may appear at any position in tile input sentence, dictionary access is tried from all the positions n. are the results of dictionary access at, posit.ion 1. For simplicity, we assume that the dictionary contains only the following words:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Dictionary Lookup", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "\"i~ ~ (large),\"", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Dictionary Lookup", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "\";k2 ~lJ. ~['.~ ~: (mainframe),\" \"a]'~ (computer)\", \"~Ig ~ f~ (eomput.ing facility),\"", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Dictionary Lookup", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "and \",~R~ (facility).\"", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Dictionary Lookup", | |
| "sec_num": "2.2" | |
| }, | |
| { | |
| "text": "The most common method for dictionary lookup is to use an index structure called TRIE (see Figure 4 ). The dictionary lookup begins with the root node. The hatched nodes represent.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 91, | |
| "end": 99, | |
| "text": "Figure 4", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Using TRIE", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "the terminal no(tes that correspond to dictionary entries. At position 1 in tile sentence ab(we, I, wo ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Using TRIE", | |
| "sec_num": "2.3" | |
| }, | |
| { | |
| "text": "Our idea is to use t, he b)dex stsuct,m'e de w~loped by Abe and Corasick to find muli;iple sl,rings in a text. '~ (COlIIplll;cT),\" and so on. Thus, the nmnt)er of dictionary node ac(:esses is greatly reduced. Ill many Japanese tnorphok)gical analysis systems, the dictionary ix held in the secondary ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Eliminating Backtracking", | |
| "sec_num": "2.4" | |
| }, | |
| { | |
| "text": "A TI{IF, index with fail pointers is created in the following two steps:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Constructing TRIE with fail pointers", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "1. Create a TI{IE iudex, and", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Constructing TRIE with fail pointers", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "Other Cosiderations 2. Calculate a fail pointer of each node in the TRIE.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "2.5", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Theoretically there is a I)ossiMlity of 1)rm,ing dictionary lookup by using the state set at. posi- instead of a simple TRIE. Altough a B-tree has much less nodes than a TRIE and thus the number of secondary storage accesses can be significantly reduced, it still backtracks to the next character position and duplicate matching is inevitable.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "2.5", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Step 1 is well known, we will describe only Step 2 here.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Since", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "]\"or each node n, Step 9 given the value fail(n). .\" 3 Figure 6 shows the complete TRIE with tile fail pointers. traditional TRIE and was 27% faster in CPU time. The CPU time was measured with all the nodes in the main memory.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 55, | |
| "end": 63, | |
| "text": "Figure 6", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Since", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "For the computer manuals, the reduction rate was a little larger. This is attributable to the fact that computer manuals tend to contain longer, more technical terms than newspaper artMes. Our method is more effective if there are a large number of long words in a text.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Since", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The algorithm for consulting the dictionary is quite simple: ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Dictionary access", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "We applied the TRIE with fail pointers to our 70,491-word dictionary for Japanese morphological analysis (in which the average word length is 2.8 characters) and compared it with a conventional TRIE-based system, using two sets of data: newspapers articles (44,113 characters) and computer manuals (235,104 characters). The results are shown in Table 1 .", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 345, | |
| "end": 352, | |
| "text": "Table 1", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Experimental results", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "The tables show both the number of node accesses and the actual CPU time. For the newspaper articles, our method (marked as TRIE w/ FP) had 25% fewer node accesses than than the SThis information is redundant, because one can look up every possible word by following the fail pointer, llowever, if the nodes are in secondary storage, it is wort, h having the length information within the node to minimize the disk access.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Experimental results", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "We have proposed a new method of dictionary lookup for Japanese morphological analysis.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "The idea is quite simple and easy to implement with a very small amount of overhead (a fail pointer and an array of length l to each node). For large l, ermiriology dictionaries (medical, chemical, and so on), this method will greatly reduce tile overhead related to dictionary access, which dominates the efllciency of practical Japanese morphological analyzers. Fast Japanese morphological analyzers will be crucial to the success of statistically-based language analysis in the near fllture (Maruyama et al. 1993 ).", | |
| "cite_spans": [ | |
| { | |
| "start": 494, | |
| "end": 515, | |
| "text": "(Maruyama et al. 1993", | |
| "ref_id": "BIBREF2" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": ". IIisamitsu, T. and Nitro, Y., ]991: \"A Uniform 'h'eatment of Ileuristic Methods for Morphological Analysis of Written", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
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| "ref_entries": { | |
| "FIGREF0": { | |
| "uris": null, | |
| "text": "22.-I-ft1:*i1:19 I~:19 \u00a9:76 -I-~I-: :19 ~:. :78 ix:9 0:29 ]'2:63 o :100 Sample input/output of JMA", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "uris": null, | |
| "text": "example, in the sentence fragment: ill Figure 3, \"7..~ ~{'/. (large)\" and \"J~ ~I' J. ~i[ ~i ~ (mainframe)\"", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "uris": null, | |
| "text": "words, \"Jq~.eJ. (large)\" and \" )k:)l'-I.}i].~:~.~ (mainframe),\" are found. Then, the starting position is advanced 1;o the second character in the text; and the dictionary lookup is tried again. In this case, no word is found, because there are no words thai, begins ~Actual' dictionaries' '\u00a31so co,train i')'C (big),\" \" ,b~,! (type),\" \"~'1\" (measure),\" \"i{l'~: (compute),\" \"~'~: (cwdm.),\" \"~ (m,.:hi.~.),\" \"~b~ (~.~t,,bU.~h),\" ,.,,i \"~;;i (p,.,,~ ...... ).\" with \"~{'.1.\" in the dictionary. The start, lug position is l, hen set I,o 3 and t.rled again, and this ,.i,,~ th,. words \"al~:~ (,:,lnlp,,Ce,.y and \"i}t~'; k)~;{'~{i[i (comput.ing facilit,y)\" are obtained. 'e The problem here ix (,hal;, even though we know that, 1,here is \"TQ){l!}i[.~])~ (ma.in[rarne)\" al, posit;ion I, we look up \"}}[~{:t~ (computer)\" again. Since \"iil~:~.~: (computer)\" is a snhstring of \"9'4~{~iI'~;)1~ (n-lainframe),\" we know that, t;he word \"~,i]~,i~ (compul:er)\" exists at, posit;ion 3 as soon as we lind \"X~{~}~[~3,,i~ (lnainframe)\" at i)o-. sit;ion I. Therefore, going back l;o 1;he root node at position 3 and trying mat;citing all over again means duplicatAng our efforts unnecessarily.", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF3": { | |
| "uris": null, | |
| "text": "Figure 5 shows l;he TRII!; with a point.er called t;he fail pointer associated with the node corresponding to l;he word \"7)k/~I T/ ~'[~2~: (mail fxa.nm) ' (the rightmost, word in Lhe first row). When a match st;re'Ling al, position n reaches I, his node, it is gnaranl,eetl that tile sl.ring \"~,ilJ)i~,~\" exists starting at position n -t-2. Therefore, if the next character in the input sentence does not mat,oh any of the child nodes, we do not go b~ck to the root but go back to the node corresponding 1,o this substring by following t, he fail pointer, and resume matching from this node. For the input sentence in l,'igure 3, l.he dict, ionary access proceeds as indica.ted by the dot, t;ed line in I.he Figure 5, 13n(ling the words \")<~{t! (la.rge),\" \"g<~{t[}][#:~.~ (mair,[','ame),\" \"]i[~'~:", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF4": { | |
| "uris": null, | |
| "text": "storage, a.nd t, herefore the number of dictionary ~Wh,, r,,:~, ch,~t \"X~{'~iil~A:~.~ (,,**i,,~'~,-,,,0\" w,~.~ re,,.,1 heft)re does no(. neees;sarily mean f,[ud, there is no need to l~mk up \"~{I'~:)~%.(computr.r ...),\" because at this point twa interpretat.ions, \"mainframe facilit.y\" and \"large computing facilit.y,\" are possible. J[~i~/~ -> EAGYOU-SDAN [pos=l,kow=doushi] ~f 5 f~; YJ~ 5 [~ -> \u00b0'~'\" [pos=26,kow=v_infl] ~,[iJ 5 ~k,~ 4 ~x,~ cost=300; ~] 5 ~-~,~)-4~(ff6 -> \"J'\" [pos=64,kow=jodoushi,fe={negative}] \"~\" [pos=78, kow=set suzoku_j oshi] ~ItJj~ ~'f cost=500; ~l~J~J~,~Y/ -> J[~)~ cost=999; ~1 -> \"\" [pos=48,kow=jodoushi] J~J~-~Y~,#~ cost=S00; .... ~ ~)~J~ cost=300; ~)j~y~t~ _> \"fZ o\" [pos=45,kow=aux_infl] '\"~J[J~ cost=a00; Some of the grammar rules 'I'I{IE structure with ]hil pointers node accesses dominates the performance of the overall system.", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF5": { | |
| "uris": null, | |
| "text": "tion n. For example, if no noun can follow rely of the states in the current state set, there is no need to look up nouns. One way to do this pruning is to associate with each node a bit vector representing the set of all parts of speech of some word beyond this node. [f the intersection of the expected set of parts of speeche an(t the possi. bilities beyond this node is empty, the expansion of this no(te can be pruned. In general, however, almost every character position t)redicts most of the parts of speech. Thus, it is common practice in Japanese morphok)gical analysis to h)ok up every possible prefix at every character position. Hidaka et al. (1984) used a modified l{-tree", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF6": { | |
| "uris": null, | |
| "text": "In the following algorittlm, for'ward('n, c) denotes the chikl node of the node 'n whose associated character is c. If there is no such node, we define forward(n, e) = hi]. Root is the root no(le of the T1HF,.", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF7": { | |
| "uris": null, | |
| "text": "1 j'ail(l~oot) ~-leooe 2-2 for each node ft. of depth 1, fail(n) ~ lSmt 2-a re,. e~,:l~ depth d--1,2, ..., 2-3-1 for each node. n with depLh d, 2-3-I-I for each child node rn of n (where m = forward(n, c:)), fail(m) +--f(fail(n), c). l[ere, ]'(n, c) is defined as follows: fail(',,.) if forward(n, c) 5L nil f('n, c) = f(fail(n),c) if forward(n, c:) = nil & n ~ Root t~oot otherwise If tile node corresponds to the end of some word, we record the length l of the word in the node. For example, at the node that corresponds to the end of the word \"~:~t'~:~ (mainframe)\", I = 5 and l = 3 are recorded because it is the end of both of the words \"~].~:~ (mainframe, l = 5)\" and \"~l'-~-~ (computer, l = 3)", | |
| "num": null, | |
| "type_str": "figure" | |
| }, | |
| "FIGREF8": { | |
| "uris": null, | |
| "text": "1 n +--Root 2 for each character position i = 11,2, ...k, 2-1 while n 7~ Root and forward(n, ci) = nil do n ~--fail(n) 2-2 n = forward(n,cl) 2-3 ifn is the end of some word(s), output them where ci is the character at position i.", | |
| "num": null, | |
| "type_str": "figure" | |
| } | |
| } | |
| } | |
| } |