{ "paper_id": "C92-1019", "header": { "generated_with": "S2ORC 1.0.0", "date_generated": "2023-01-19T12:33:11.835194Z" }, "title": "", "authors": [ { "first": "Keh-Jiann", "middle": [], "last": "Chen", "suffix": "", "affiliation": {}, "email": "" }, { "first": "Shing-Lluan", "middle": [], "last": "Liu", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": "Chinese sentences are composed with string of characters without blanks to mark words. However the basic unit for sentence parsing and understanding is word. Therefore the first step of processing Chinese sentences is to identify the words. The difficulties of identifying words include (l) the identification of complex words, such as Determinative-Measure, reduplications, derived words etc., (2) the identification of proper names,(3) resolving the ambiguous segmentations. In this paper, we propose the possible solutions for the above difficulties. We adopt a matching algorithm with 6 different heuristic rules to resolve the ambiguities and achieve an 99.77% of the success rate. The statistical data supports that the maximal matching algorithm is the most effective heuristics.", "pdf_parse": { "paper_id": "C92-1019", "_pdf_hash": "", "abstract": [ { "text": "Chinese sentences are composed with string of characters without blanks to mark words. However the basic unit for sentence parsing and understanding is word. Therefore the first step of processing Chinese sentences is to identify the words. The difficulties of identifying words include (l) the identification of complex words, such as Determinative-Measure, reduplications, derived words etc., (2) the identification of proper names,(3) resolving the ambiguous segmentations. In this paper, we propose the possible solutions for the above difficulties. We adopt a matching algorithm with 6 different heuristic rules to resolve the ambiguities and achieve an 99.77% of the success rate. The statistical data supports that the maximal matching algorithm is the most effective heuristics.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "Chinese sentences arc cx)mposed with string of characters without blanks to mark words. However the basic unit for sentence parsing and understanding is word. Therefore the first step of processing Chinese sentences is to identify the words( i.e. segment the character strings of the sentences into word strings).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "Most of the current Chinese natural language processing systems include a processor for word identification. Also there are many word segmentation techniques been developed. Usually they use a lexicon with a large set of entries to match input sentences [2, 10, 12, 13, 14, 21] . It is very often that there are many l~)ssible different successful matchings. Therefore the major focus for word identification were on thc resolution of ambiguities. However many other important aspects, such as what should be done, in what depth and what are considered to be the correct identifications were totally ignored. High identification rates are claimed to be achieved, but none of them were measured under equal bases. There is no agreement in what extend words are considered to be correctly identified. For instance, compounds occur very often in Chinese text, but none of the existing systems except ours pay much attention to identify them. Proper name is another type of words which cannot be listed exhaustively in the lexicon. Therefore simple matching algorithms can not successfully identify either compounds or proper names. In this paper, we like to raise the ptx~blems and the difficulties in identifying words and suggest the possible solutions.", "cite_spans": [ { "start": 254, "end": 257, "text": "[2,", "ref_id": "BIBREF1" }, { "start": 258, "end": 261, "text": "10,", "ref_id": "BIBREF9" }, { "start": 262, "end": 265, "text": "12,", "ref_id": "BIBREF11" }, { "start": 266, "end": 269, "text": "13,", "ref_id": "BIBREF12" }, { "start": 270, "end": 273, "text": "14,", "ref_id": "BIBREF13" }, { "start": 274, "end": 277, "text": "21]", "ref_id": "BIBREF20" } ], "ref_spans": [], "eq_spans": [], "section": "Introduction", "sec_num": "1." }, { "text": "As we mentioned in the prevkms chapter, thebasic technique to identify the words is by matching algorithms. It requires a well prepared lexicon with sufficient amount of lexical entries which covers all of the Chinese words. Iqowcver such a large lexicon is never existing nor will be composed, since the set of words is open ended. Not only because the new words will be generated but because there are unlimited number of compounds. Most of the word identification systems deliberately ignore the problem of compounds and leave the problem unsolved until the stage of parsing. We don't agree their view points and believe that different type of Compounds should be handled by the different meth~Ms at different stages. Some types of the compounds had better to be handled before parsing, for they require different grammatical representations and idcntificalion strategies compared with the parsing of phrase structures. On the camtrary, if the lnorphological rules [or compounds have the ~me representation as the phrase structure rules, it is better to be identified at parsing stage. We will di~uss this issue in more details in the later sections.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Difficulties in the Identification of Words", "sec_num": "2." }, { "text": "The other problem is that ambiguous segmentations frequently tv.:cur during thc processing of word matching. It is because that very often a multisyllabic word contains monosyllabic words as its components. We have to try the different strategies to re~flve such ambiguities.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Difficulties in the Identification of Words", "sec_num": "2." }, { "text": "Many problems need to be ~lved, but first of all a lexicon shotdd be composed for the matching algorithm.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Difficulties in the Identification of Words", "sec_num": "2." }, { "text": "According to Liang's [14, 15] definition, word is a smallest,meaningful, and freely used unit. It is the basic processing unit fur Chinese natural language processing. Since there is no morphological features as word segmentation marks, we have to adopt such a wtgue definition of the Chinese word. Liang [151 also propose a word segmentation standard. However some of his view points are debatable and self contradictory. In fact it is almost impossible to define a standard for COrrect identification. Thcrefl)re instead of proposing a standard, we propose a criterion which should be followed by a good word segmentation algorithm. It is that a good segmentation algorithm should be able to produce a result which is suitable and sufficient for the propose of later processing, such as parsing and understanding.", "cite_spans": [ { "start": 21, "end": 25, "text": "[14,", "ref_id": "BIBREF13" }, { "start": 26, "end": 29, "text": "15]", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "What are the Set of Words", "sec_num": "2.1" }, { "text": "The set of words is open ended. ~lherefore the existing lexicons contain lexical entries which vary from 40 to 120 thousands. A large lexicon usually includes many compounds as well as many proper names, for it is hard to distinguish a word and a compound. Fbr systems with a small lexicou, they might not perform worse than systems with a large lexicon, if they incorporate algorithms to identify compounds and proper names. However on the other hand there is no harm to have a large lexicon, once there is a way of handling the ambiguities, since statistically a large lexicon has the better chance to match words as well as producing ambiguous segmentations. Therefore we have the follow principle to collect the word set for the purpose of word segmentation.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "What are the Set of Words", "sec_num": "2.1" }, { "text": "qqm lexicon should contam as many as possible words. If there is a doubt whether a string of characters is a word or a compound, you can just collect it as an entry.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Principle for composing a lexicon:", "sec_num": null }, { "text": "Currently we have a lexicon of around 90 thousands entries, and keep updating for new words. A lexicon with such a s~e of course would still leave out many compounds and proper names. We use this lexicon to match the Chinese text, the result of the algorithm is a sequence of words defined in the lexicon. (1) is an instance of result.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Principle for composing a lexicon:", "sec_num": null }, { "text": "(1) a. jieshuoyuan Jau Shian-Ting yindau tamen interpreter Jau Shian-Ting guide them 'q'he interpreter Shian-Ting Jau guided them.\" b. jieshuo-yuan-Jau-Shian-Ting-yindau-tamen However we can see that ~me of the compounds and proper names are not identified as shown in (1lb. They were segmented into words or characters. Therefore at later stage those pieces of segments should be regrouped into compounds and proper names. We will discuss tile issue at next two sections.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Principle for composing a lexicon:", "sec_num": null }, { "text": "1here are many different type of compounds in Chinese and should be handled differently [3, 6, 7, 11, 17, 191. a. determinative-measure compounds (DM) A determinative-measure compound is composed of one or more determinatives, together with an optional mcasm'e.", "cite_spans": [ { "start": 88, "end": 110, "text": "[3, 6, 7, 11, 17, 191.", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "Compmmds", "sec_num": "2.2" }, { "text": "(2) je san ben this three CL \"these three\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Compmmds", "sec_num": "2.2" }, { "text": "It is used to determine the reference or the quantity of the noun phrase that co-~w.curs with it. De~ite the fact that I~)th categories of determinatives and measures are closed, the comhinations of them are not. However the set of DMs is a regular language which can be expressed by regular expressions and reCOgnized by finite automata [19] . Mo [191 also point out that the structure of I)Ms are exocentric. They are hardly similar to other phrase structures which are endocentric and context-free and can bc analyzed by head driven parsing strategies. Therefore we suggest that tile identification of DMs should be done in parallel with the identification of common words. There are 76 rules for DMs which covers ahnost all of the DMs [19] . Dor word identification, those rules function as a supplement for the lexicon, which works as ff the lexicon contains all of the DMs. We will show the test result in the section 3.3.", "cite_spans": [ { "start": 338, "end": 342, "text": "[19]", "ref_id": "BIBREF18" }, { "start": 739, "end": 743, "text": "[19]", "ref_id": "BIBREF18" } ], "ref_spans": [], "eq_spans": [], "section": "Compmmds", "sec_num": "2.2" }, { "text": "In Chinese many verhs can be reduplicated to denote all additional meaning of flying the actions gently and relaxedly(3)[7].", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "b. Reduplications", "sec_num": null }, { "text": "(3) tiau tiau wu jump jump dance \"dance a little\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "b. Reduplications", "sec_num": null }, { "text": "This kind of molphological construction will m)t change the argument structure of the verbs, but do change their s3mtactic behavior. For instance, the reduplications can not cooccur with the adjuncts of postverbal location, aspect marker, duration, and quantifier [171. In [17] , they derived 12 different reduplication rules which cover the reduplication construction of verbs. Ill addition, there are 3 rules for the reduplication of DMs and 5 rules for A-not-A questions formation.", "cite_spans": [ { "start": 273, "end": 277, "text": "[17]", "ref_id": "BIBREF16" } ], "ref_spans": [], "eq_spans": [], "section": "b. Reduplications", "sec_num": null }, { "text": "The identification of the reduplication construction should be done after the words have been identified, since it is better to sec the words and then check whether part of the words has been reduplicated. It is a kind of context dependent process, so a separated process other than the process for DMs or Phrase structures, should be incorporated. Since the grammatical representation of derived words is the same as the representation of phrase structures, wc suggest that the identification of the dcrivcd words are better to be done at tile parsing stage. Furthermore, the Ixoundaries of the derived words ave syJttactically ambigu(ms. They can not he identified without checking the contextual information.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "b. Reduplications", "sec_num": null }, { "text": "Proper names (w.cur very frequently in all kinds of articles. ~[lte identification of proper nanlcs become one of the most difficult problems in Chinese natural language processing, since we can not exhaustly list all t,f the ptToper name in the lcxiceu. Also there is no morphologieal nor punctuation makers to denote a proper name. Besides that a proper name may contain a suhstring of common words. It makes the identification of the proper names even harder. The only clue might be usetul in identifying proper names is the occurrences nf Ix)and nu)ll)heules. Usually each Chinese character is a meaningful unit. Some of them can I;e used freely as a word. Some are not; they have to be combined with other characters to form a word. Such characters can not be used freely as words, are named bound nmr-. phemes. If bound moqflmmes occur after word matching process, it means that there are derived words or proper names occurred iu the text and have not been identified. The semantic classification of morphemes can be utilized to identify the different type of proper names. For instance,in [11, the setofsurnames were used its a clue to identify people's names and titles. There is no general solutions so far to handle the different types of proper names. The only suggestion is that mark the proper names before identification procuss or treat the unknown strings as proper names.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Proper Names", "sec_num": "2.3" }, { "text": "For Chiuese character strings,they might have many different well fl~rmed segmentations, but ust,ally there is tndy one grammatically aud semantically sound segmemation fur ~lch sentence (7).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ambiguities", "sec_num": "2.4" }, { "text": "(7) yijing jeuglichu jiegno aheady arrangc~out result 'q'he result has come out.\" yijiug-[jengli-ehu]-jiegno yijing-ljengqichu ] -jiegun Therefore many algorithms were proposed and heuristic or statistical pretcrence rules were adopted for remlving ambiguities. However none of those rules has been thoruughly tested and provided their success rates, ht the next section, we will state our algorithm as well as tile heuristic rulesand alsoprovides the experio merit results in section 3.2 to show the success rate of each individual tall:.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Ambiguities", "sec_num": "2.4" }, { "text": "According to the discussion of the chapter 2, the picture of the word identificatinn algoxithm should be clearly its Icit[ows.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "3.Wnvd Identification Algnrithm", "sec_num": null }, { "text": "In fact trot all of the above processes were thoroughly studies, but more ov less some of them were studied and have successful results [2, 8, 12, 13, 14, 16, 19, 20, 211 . Our word identification system adopt tl,e alnlve sequence of algorithms, lint we defer the second last prlycess of finding derived words until parsing stage and tile last In'ocess of finding proper names is tempo+ ", "cite_spans": [ { "start": 136, "end": 139, "text": "[2,", "ref_id": "BIBREF1" }, { "start": 140, "end": 142, "text": "8,", "ref_id": "BIBREF7" }, { "start": 143, "end": 146, "text": "12,", "ref_id": "BIBREF11" }, { "start": 147, "end": 150, "text": "13,", "ref_id": "BIBREF12" }, { "start": 151, "end": 154, "text": "14,", "ref_id": "BIBREF13" }, { "start": 155, "end": 158, "text": "16,", "ref_id": "BIBREF15" }, { "start": 159, "end": 162, "text": "19,", "ref_id": "BIBREF18" }, { "start": 163, "end": 166, "text": "20,", "ref_id": "BIBREF19" }, { "start": 167, "end": 170, "text": "211", "ref_id": null } ], "ref_spans": [], "eq_spans": [], "section": "(u)", "sec_num": null }, { "text": "The first two steps of word identification algorithm are the word matching and disambiguation. These two processes were performed in parallel. Once an ambiguous match occurs, the disambiguation process is invoked immediately. The algorithm reads the input sentences from left to right. Then match the input character string with lexemes as well as DMs rules, If an ambiguous segmentation do occur, then the matching algorithm looks ahead two more words, then apply the disambiguation rules for those three word chunks. For instance,ha (9), the first matched word could be 'wan' or 'wancheng'. Then the algorithm will look ahead to take all of the possible combinations of three word chunks, as shown in (10), into consideration. (9) wanchengjianding haugau complete authenticate report \"complete the report about authenticating\" (10) wan~:heng-jianding wancheng-jianding-bau wancheng-jianding-baugau", "cite_spans": [ { "start": 729, "end": 732, "text": "(9)", "ref_id": "BIBREF8" } ], "ref_spans": [], "eq_spans": [], "section": "Matching algorithm and disambiguation rules", "sec_num": "3.1" }, { "text": "The disambiguation algorithm will select the first word of the most plausible chunk as the solution. In this case, it is the word 'wancheng'. The algorithm then proceeds to process the next word until all the input text been processed. '/'he most powerful and commonly used disambiguation rule is the heuristic of maximal matching [12, 13, 14, 21] . There are a few variations of the sense of maximal matching, but after we have done the experiments with each of different variations, we adopt the following maximal matching rules.", "cite_spans": [ { "start": 331, "end": 335, "text": "[12,", "ref_id": "BIBREF11" }, { "start": 336, "end": 339, "text": "13,", "ref_id": "BIBREF12" }, { "start": 340, "end": 343, "text": "14,", "ref_id": "BIBREF13" }, { "start": 344, "end": 347, "text": "21]", "ref_id": "BIBREF20" } ], "ref_spans": [], "eq_spans": [], "section": "Matching algorithm and disambiguation rules", "sec_num": "3.1" }, { "text": "The most plausible segmentation is the three word sequence with the maximal length.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 1:", "sec_num": null }, { "text": "This heuristic rules achieves as high as 99.69% accuracy and 93.21% of the ambiguities were resolved by this rule. We will see the detail statistics in the next section. However there are still about 6.79% of ambiguities still can not be re~lved by the maximal matching rule. Therefore we adopt the next heuristic rule.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 1:", "sec_num": null }, { "text": "Pick the three word chunk which has the smallest standard deviation in the word length. This is equivalent to find the chunk with the minimal value on ( L(W1)-Mean) **2 + (L(W2)-Mean)**2 + (14'W3)-Mean)**2 ,where Wl,W2,and W3 are three words in a chunk; Mean is the average length of Wl,W2.,and W3; L(W) denotes the length of the word W.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 2:", "sec_num": null }, { "text": "Heuristic rule 2 simply says that the word length are usually evenly distributed. For instance in (11), the segmentation of (lla) has the value 0, but (1 lb) has value 2. Therefore according to the heuristic rule number 2, the (lla) will be the selected solution and it is the correct segmentation. However it may happen that there are more than two chunks with the same length and variance, we need a further resolution.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 2:", "sec_num": null }, { "text": "Pick the chunk with fewer bound morphemes.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 3:", "sec_num": null }, { "text": "Pick the chunk with fewer characters in DMs.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 4:", "sec_num": null }, { "text": "That is to say the normal words get higher priority than the bound morphemes and DMs. For instances examples (12, 13) were resolved by the rule 3 and 4 respectively. (12a) and (13a) are right choices. The heuristic rules 2,3,and 4 only resolve 1.71% of the ambiguities as shown in the table 2 of the next section. After we observe the remaining ambiguities we found that many ambiguities were occurred due to the occurrences of monosyllabic words. For instances, the character string in (14a) can be segmented as (14b) or (14c), but none of the above resolution rules Can resolve this case. If we compare the correct segmentations with the incorrect segmentations, we find out that almost all of the monosyllabic word in the correct answer are function words, such as prepositions,con]unctions, as well as a few high frequent adverbs. And that the monosyllabic words in the incorrect segmentations are lower frequency words. The set of such frequently occurred monosyllabic words are shown in appendix 1. We then have the following heuristic rule.", "cite_spans": [ { "start": 109, "end": 113, "text": "(12,", "ref_id": "BIBREF11" }, { "start": 114, "end": 117, "text": "13)", "ref_id": "BIBREF12" } ], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 4:", "sec_num": null }, { "text": "Pickthechunk with the high frequentlyoccurred monosyllabic words.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 5:", "sec_num": null }, { "text": "This rule contributes 3.46% of the success of the ambiguity resolution. The remaining unsolved ambiguities are about 1.62% of the total input words. They usually should be resolved by applying real wurld knowledge or by checking grammatical validity. However it is almost impossible to apply real world knowledge nor to check the grammatical validity at this stage, so applying Markov m(v..lel is a possible solution [21] . The other solution is much simpler ,i.e. to pick the chunk with the highest accumulated frequency of words[221. It requires the frequency counts for each words only instead of word bigram or trigram which required by the Markov model.", "cite_spans": [ { "start": 417, "end": 421, "text": "[21]", "ref_id": "BIBREF20" } ], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 5:", "sec_num": null }, { "text": "Pick the chunk with the highest probability value. q~e prohability value of the sequence of words' W 1 W2 W3' can be estimated by either a) Markov model with the bigram approximation P~P(W01Wl ) * P(WI[W2) * P(W21W3) * P(W3) ; or b) Word probability accumulation P = PfWl) + t,(w2) + P(W3)", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 6:", "sec_num": null }, { "text": "Heuristic rule 6a might not be feasible, since it requires word bigram a matrix of size in the order of 10\"'10. But heuristic 6b) might not produce a satisfactory resolution. According to our experiment the success rate for 6b) is less than 70%. qlaerefore the other solution is to retain the ambiguities and resolve at the parsing stage,", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Heuristic rule 6:", "sec_num": null }, { "text": "We designed a word identification system to test the matchingalgorithm and the above mentioned heuristic rules. The lexicon for our system has about 90 thousands lexical entries plus mflimited amount of the DMs generated from 76 regular expressions. The 90 thousands lexemes form a word tree data structure in order to speed up the word matching [4, 10] .For the same reasons, DM rules are compiled first to produce a Chomsky Normal Form like parsing table. The parsing table will then he interpreted during the word matching stage [19] . 'l~vo sets of test data are randomly selected from a Chinese corpus of 20 million characters. We summarize the testing result in \"lhhle 1. qhble 2 shows the success rates and applied rates for each heuristic rule.", "cite_spans": [ { "start": 346, "end": 349, "text": "[4,", "ref_id": "BIBREF3" }, { "start": 350, "end": 353, "text": "10]", "ref_id": "BIBREF9" }, { "start": 532, "end": 536, "text": "[19]", "ref_id": "BIBREF18" } ], "ref_spans": [], "eq_spans": [], "section": "Experiment Results", "sec_num": "3.2" }, { "text": "qhe recall rate and recognition rate in the above table are defined as follows. Let NI = the number of words m the input text, N2 = the number of words segmented by the system for the input text. N3 = the number of words were correctly identified. Then the recall rate ss defined to bc N3/NI and the precision rate ks N3/N2. \"lhc dclmlta)n of the other statistical result are t~vlousJ) I'ollnwed the convention.The above testing algorithm do not include the process of handling derived ~4 wds. \"lhcrefore the above statistics do not ta)unt the mt~lakcs occurred due to the existence of derived word,,, or proper names.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Experiment Results", "sec_num": "3.2" }, { "text": "We can sec that the maximal matching algorithm is the most effective hcunst~t~, lbcrc are 10311 number of ambiguities out of 17404 occurrences of the seg- ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Experiment Results", "sec_num": "3.2" }, { "text": "From the statistical results shown in table 2, it is clear that the ma.,dmal matching algorithm is the most useful heuristic method. Most of the mistakes caused by this heuristic are due to the occurrences of the words which are composed by two subwords. Those words are needed to have further investigations. If we want to further improve our system's performance, it seems that employing lexically dependent rules is unavoidable.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.Discussions and Concluding Remarks", "sec_num": null }, { "text": "The errors caused by the heuristic rule 2are due to t he cases of a three character word followed by a monosyllabic word and which can he divided into two hisyllabic words, for instance (15).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.Discussions and Concluding Remarks", "sec_num": null }, { "text": "(15) tzai shia san jou at down three week \"in the followlug three week\"", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.Discussions and Concluding Remarks", "sec_num": null }, { "text": "[tzai-shiasanjou] [tzaishia-sanjou] Such mistakes can be avoided by giving the second bisyllabic words a lexically dependent marker which denotes that a low priority is given to this word when the heuristic rule 2 is applied. 'FILe heuristic rules #3 and #4 are the most reliable disambiguation rules. However they only contribute 0.53% of the disambiguation processes.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.Discussions and Concluding Remarks", "sec_num": null }, { "text": "The heuristic rule #5 is ugeful, but the priority values for each high frequent monosyllabic word has to be carefully rearranged in order to reduce possible mistakes [181.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.Discussions and Concluding Remarks", "sec_num": null }, { "text": "The heuristic title #6 needs to be further studied. It will be much more easier to use the bigram or trigram based on gt-ammatical categories instead of the word bigram or the simple accumulation of the word frequencies. It will be the future study.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.Discussions and Concluding Remarks", "sec_num": null }, { "text": "About the identification of the proper names, it requires a further investigation on the results of the proper names 'after segmentation algorithm is applied.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "4.Discussions and Concluding Remarks", "sec_num": null }, { "text": "Acrt!s DE COLING-92, NANIES, 23-28 Aot'n' 1992 1 0 3 PROC. OV COLING 92, NANIES, A~,IG. 23.-28, 1992", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null } ], "back_matter": [], "bib_entries": { "BIBREF0": { "ref_id": "b0", "title": "A Multiple-Corpus Approach to Identi cation nf Chinese Surname-Names", "authors": [ { "first": "J", "middle": [ "S" ], "last": "Chang", "suffix": "" } ], "year": 1991, "venue": "Proc. of Natural Language Processing Pacific Rim Symposium", "volume": "", "issue": "", "pages": "", "other_ids": {}, "num": null, "urls": [], "raw_text": "J. S. 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OF COLING-92, NAI~rES, Auo. 23-28, 1992", "links": null } }, "ref_entries": { "FIGREF0": { "num": null, "uris": null, "text": "fang-bu-fangshin put not stop-worry \"stop worrying or not\" A-nof.A constructien is a kind of reduplication constrution. Therefore the technique for the idemification of reduplication is aim applicable for the identification of the A-not--A construction.d. Derived WordsA derived word is at compound which (x)mposed with a word of stem and a prefix or a suffix[ 11 ]. I)eriva tive affixes are very productive in Mandarin Chinese.(5) difang-shing place quality \"localityr\"Those affixes usually are bound morphemes. In [11], we collect a sct of most frcqucntly occurred affixes and study their morphological behaviors. We found that there are syntactic and semantic restrictions bctween modifiers and heads. Such a nrorphok)gical patterns can bereprcsentedintermsoflnfonnation-. based Case Grammar[5], which is also the grammatical formalism adopted for representing Chinese phrase structures in our parsing system[5,ll]. Following is an example of representation.(6) shing Semantic:meaning: equivalent of \"-NESS\", \"-lq~(\", for cxprcssing abstract notation Syntactic: category: Nad feature: bound; I + N,--V] constraints: MR: {Vhl, V[ + transitivc [, N} < < *", "type_str": "figure" }, "FIGREF1": { "num": null, "uris": null, "text": "11) yianjiou shengminchiyuan research life origin \"to investigate the origin of life\" a. [yianjiou-shengminl-chiyuan b. [yianjiousheng-min]-chiyuan", "type_str": "figure" }, "FIGREF2": { "num": null, "uris": null, "text": "shietiau shang shoushiu jiau mafan negotiate up procedure more troublesome AcrEs DE COLING-92, NANTES. 23-28 hOt'q\" 1992 1 0 4 PROC. OF COLING-92, Nhr\u00a2rEs, AUG. 23-28, 1992 \"In negotiation, the process is more complicated.\" a. shietiau-[shang-shoushiu]-jiau-mafan b. shietiau-[shangshou-shiu]-iiau-mafan (13) ta benren he serf \"he himself\" a. ta-benren b. taben-ren", "type_str": "figure" }, "FIGREF3": { "num": null, "uris": null, "text": "the precise number of the infected\" ganran-[de-chiuanshr]-renshu-shieshialai ganran-[dechiuan-shr]-renshu-shieshialai", "type_str": "figure" }, "TABREF0": { "html": null, "text": ". A-not-A constructionA-not-A constructions are commonly used in Chinese to form a questiou[13,71. Aswe mentioned before, for A--not~A construction, there are 5 different rules for reduplicating part of tile verbs and coverbs[17].", "content": "", "num": null, "type_str": "table" }, "TABREF2": { "html": null, "text": "OF COLING-92, NANTES, Aua. 23-28. 1992 mentations. It counts 58.94% of the total segmentations and 93.21% of ambiguities were resolved by this heuristics.", "content": "
ACRES DECOL]NG-92, NANTES. 23-28 AO(;r 1992l 0 5PROC.
", "num": null, "type_str": "table" }, "TABREF3": { "html": null, "text": "Testm results", "content": "
Sample 1Sample 2Total
# of sentences83319682801
# of characters84552087929334
# of v~ords50851240917494
+ ,
# of words identified by the system50761239917475
# of correct identifications5O641237017434
. ,
recall rate99.58%99.69%99.66%
precision rate99.76%99.77%99.77%
guLe2
~e4
H~mt~
Hcalrllt~
Rule6
", "num": null, "type_str": "table" }, "TABREF4": { "html": null, "text": "The success rates of the heuristic rules", "content": "
Sample 1S+mple 2Total
# of laentdicattom# of error~succe~ts rate$ of mentilioaiom i of erron ~ucoms rate i r of klentificltlor# of errors suCCX~s rate
28751399.58%69381799,75%98133099.09%
3648~.89%74395.98%110793.64%
00100%50100%80100%
18O100%32196.86%50198.00%
104298,08%.2381294.96%3421495 90%
48156878%1093666,97%
", "num": null, "type_str": "table" } } } }