Teburin Abubuwan Ciki
- 1. Gabatarwa
- 2. Bayanan Baya
- 3. Tsarin Lamb
- 4. Sakamakon Gwaji
- 5. Misalin Tsarin Bincike
- 6. Ayyuka na Gaba & Jagorori
- 7] Bayanan
1. Gabatarwa
Rashin tabbacin ƙamus yana tasowa a zahiri a cikin harsuna lokacin da jerin shigarwa suka dace da jerin alamomi masu yawa. Na'urorin nazarin ƙamus na al'ada kamar lex suna tilasta fifikon alamomi na musamman, suna tilasta masu haɓakawa su zaɓi fassarar ɗaya fiye da wasu. Wannan hanyar ta kasa a yanayin da ma'ana ta dace inda yakamata a fassara ɗayan jeri daban-daban dangane da mahallin syntactic.
Lamb (Rashin Tabbacin Ƙamus) yana magance wannan iyaka ta hanyar samar da zane-zane na nazarin ƙamus waɗanda ke ɗaukar duk yiwuwar jerin alamomi. Sannan na'urorin rarraba za su iya sarrafa waɗannan zane-zane don watsi da jerin da ba su da inganci, suna yin nazarin ƙamus mai ma'ana tare da daidaiton daidai.
2. Bayanan Baya
2.1 Nazarin Ƙamus na Al'ada
Ma'aunin IEEE POSIX P1003.2 ya bayyana kayan aikin lex da yacc waɗanda suka zama bututun al'ada:
- lex: Yana samar da na'urorin nazarin ƙamus tare da rikitarwar lokaci $O(n)$
- yacc: Yana samar da na'urorin rarraba waɗanda ke sarrafa jerin alamomi
Hanyoyin al'ada suna tilasta fifikon alamomi na musamman, suna haifar da fara daidaita alamomi kamar "gaskiya" da "ƙarya" a matsayin alamomin BOOLEAN maimakon IDENTIFIERS, ko da yaushe lokacin da mahallin syntactic zai ba da izinin na ƙarshe.
2.2 Hanyoyin Ƙididdiga
Samfurori na ƙididdiga kamar Hidden Markov Models (HMMs) za su iya riƙe rashin tabbas amma suna buƙatar horo mai ƙarfi kuma ba sa ba da garanti na yau da kullun. Don harsunan shirye-shirye da harsunan ƙayyadaddun bayanai, wannan rashin tabbas yana sa su zama marasa amfani.
3. Tsarin Lamb
3.1 Zane na Nazarin Ƙamus
Lamb yana gina zane mai jagora (DAG) inda nodes ke wakiltar matsayi a cikin jerin shigarwa kuma gefuna suna wakiltar alamomi. Zanen yana wakiltar duk yiwuwar rarraba alamomi, yana ba da damar bincike mai inganci ta na'urorin rarraba.
3.2 Tushen Lissafi
An ayyana zanen nazarin ƙamus $G = (V, E)$ inda:
- $V = \{0, 1, ..., n\}$ yana wakiltar matsayi a cikin jerin shigarwa mai tsayi $n$
- $E \subseteq V \times V \times T$ inda $T$ shine saitin nau'ikan alama
- Gefen $(i, j, t)$ yana wanzu idan jeri daga matsayi $i$ zuwa $j$ ya dace da alamar $t$
Algorithm na ginin zanen yana da rikitarwar lokaci $O(n^2 \cdot |R|)$ inda $|R|$ shine adadin maganganu na yau da kullun a cikin ƙayyadaddun harshe.
4. Sakamakon Gwaji
An gwada Lamb akan ƙayyadaddun harsuna masu rashin tabbas ciki har da harsunan shirye-shirye tare da kalmomi masu ma'ana da guntuwar harshe na halitta. Zanen nazarin ƙamus ya yi nasara ya ɗauki duk ingantattun rarraba alamomi, tare da rarraba kawar da jerin da ba su da inganci. Nazarin aiki ya nuna ƙarin kuɗi mai karɓuwa idan aka kwatanta da na'urorin lex na al'ada, tare da girman zanen yana girma daidai gwargwado tare da tsawon shigarwa a yanayin aiki.
Ma'aunin Aiki
Lokacin Gina Zane: $O(n^2 \cdot |R|)$
Amfani da Ƙwaƙwalwar Ajiya: Girma mai daidai gwargwado tare da girman shigarwa
Warware Rashin Tabbaci: 100% daidaiton yau da kullun
5. Misalin Tsarin Bincike
Yi la'akari da jerin shigarwa masu rashin tabbas "whiletrue":
- Na'urar lex ta al'ada: Koyaushe tana rarraba alamomi azaman WHILE + BOOLEAN
- Lamb: Yana samar da zane tare da hanyoyin WHILE+BOOLEAN da IDENTIFIER
- Na'urar Rarraba: Zaɓi jerin inganci dangane da mahallin syntactic
Wannan yana ba da damar fassarar mai ma'ana inda "whiletrue" zai iya zama alamar ganewa a cikin mahallin sanyawa amma jerin kalma mai mahimmanci a cikin tsarin sarrafawa.
6. Ayyuka na Gaba & Jagorori
Hanyar Lamb tana da babbar yuwuwa a cikin:
- Harsuna Na-Yanki (DSLs): Rike rashin tabbacin ƙamus a cikin harsunan dokokin kasuwanci
- Sarrafa Harshe na Halitta: Gina gadar tsakanin sarrafa harshe na yau da kullun da na halitta
- Nazarin Shirye-shirye: Tallafawa kayan aikin gyare-gyare waɗanda ke buƙatar fassarori da yawa
- Muhallin Ci Gaba Haɗe: Samar da ra'ayoyin rarraba alamomi da yawa na ainihi
Aikin gaba ya haɗa da inganta algorithms na ginin zane da haɗawa da dabarun rarraba ƙari.
7. Bayanan
- Aho, A. V., Lam, M. S., Sethi, R., & Ullman, J. D. (2006). Compilers: Principles, Techniques, and Tools.
- Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition.
- IEEE POSIX P1003.2 Standard (1992).
- Kleene, S. C. (1956). Representation of events in nerve nets and finite automata.
Binciken Kwararre: Juyin Juya Halin Rashin Tabbaci
Gaskiyar Asali
Lamb yana wakiltar sauyin tsari daga ƙayyadaddun nazarin ƙamus zuwa na bincike. Yayin da kayan aikin al'ada kamar lex da flex suka tilasta warware rashin tabbas da wuri ta hanyar tsarin fifiko mai ƙarfi, Lamb yana ɗaukar rashin tabbas a matsayin ainihin kadarorin harshe. Wannan hanyar tayi daidai da ra'ayin falsafa cewa mahalli, ba ƙa'idodin da aka ƙaddara ba, yakamata ya tuka fassarar—ra'ayi wanda yayi daidai da hanyoyin koyon inji na zamani kamar tsarin gine-ginen transformer a cikin sarrafa harshe na halitta.
Kwararar Ma'ana
Ci gaban fasaha yana da kyau: maimakon tilasta yanke shawara na rarraba alamomi a matakin ƙamus, Lamb yana jinkirta warwarewa zuwa lokacin rarraba inda cikakken mahallin syntactic yake samuwa. Wannan rabuwar damuwa yana bin falsafar Unix na yin abu ɗaya da kyau—nazarin ƙamus yana haifar da yuwuwar, rarraba yana kawar da abubuwan da ba za su iya yiwuwa ba. Zanen nazarin ƙamus yana aiki azaman ƙaƙƙarfan wakilcin sararin bincike, kama da yadda rarraba ginshiƙi ke sarrafa rashin tabbacin syntactic a cikin sarrafa harshe na halitta.
Ƙarfi & Kurakurai
Ƙarfi: Tabbacin daidaito na yau da kullun, kawar da hasashe na ƙididdiga, da tallafawa harsuna masu ma'ana na gaske. Ba kamar samfurori na ƙididdiga waɗanda ke buƙatar faɗaɗa bayanan horo (kamar yadda aka lura a cikin littafin Hidden Markov Model), Lamb yana ba da sakamako mai ƙayyadaddun. Hanyar tana da mahimmanci musamman ga harsunan da suka keɓance wa yanki inda bayanan horo suka yi ƙaranci amma ƙayyadaddun ƙayyadaddun suna da daidaito.
Kurakurai: Rikitarwar $O(n^2 \cdot |R|)$ na iya zama matsala ga manyan shigarwa, ko da yake marubutan sun lura da girma mai daidai gwargwado a aikace. Mafi mahimmanci, hanyar tana canza rikitarwa ga masu haɓaka na'urorin rarraba waɗanda dole ne yanzu su riƙe hanyoyin rarraba alamomi da yawa. Wannan na iya haifar da fashewar haɗin gwiwa a cikin harsuna masu rashin tabbas, mai kama da ƙalubalen da ake fuskanta a cikin tsarin rarraba harshe na halitta na farko.
Abubuwan Bincike masu Aiki
Masu ƙira harshe yakamata su karɓi hanyoyin Lamb-style don sabbin harsuna na-yanki inda ma'ana ta dace. Kayan aikin yana da mahimmanci musamman ga harsuna tare da yankuna da aka haɗa, kamar SQL a cikin harsunan shirye-shirye, ko harsunan samfuri da ke haɗa lamba da alama. Ayyukan da suka wanzu za su iya amfana da Lamb a matsayin matakin kafin sarrafawa don kayan aikin gyare-gyare waɗanda ke buƙatar fahimtar fassarori da yawa na lambar gadon. Yakamata al'ummar bincike su bincika hanyoyin haɗin gwiwa waɗanda ke haɗa garanti na yau da kullun na Lamb tare da matsayi na ƙididdiga na yiwuwar fassarori, mai yuwuwa suna jawo wahayi daga dabarun binciken katako da ake amfani da su a cikin fassarar harshe na inji.
Wannan aikin yana haɗawa da manyan abubuwan da suka faru a cikin sarrafa harshe. Kamar yadda CycleGAN (Zhu et al., 2017) ya nuna cewa fassarar hoto mara haɗin gwiwa zai iya yin nasara ba tare da kulawar musamman ba, Lamb ya nuna cewa nazarin ƙamus zai iya yin nasara ba tare da tilastawa warware rashin tabbas ba. Duk hanyoyin biyu suna ɗaukar yawan yanayin yankunansu maimakon yin yaƙi da shi. Tunanin zanen nazarin ƙamus kuma zai iya ba da labari ga bincike a cikin haɗin shirye-shirye, inda bincika fassarori da yawa na ƙayyadaddun rashin tabbas zai iya haifar da ƙarin samar da lamba mai ƙarfi.