Part of Speech and Morphology

Morphological ANNOT objects

The Teneo NL Analyzers include Part-of-Speech (POS) Taggers and Morphological Analyzers for a number of languages; the POS and Morphological Input Processors are described further here.

The NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number and verb tense.

The languages with available Morphological ANNOT Language Objects are listed and can be accesses in the menu.

Chinese (Mandarin)

The Chinese Analyzer input processor uses a state-of-the-art tokenizer and morphological analyzer to split the user inputs into tokens and annotate them with morphological and syntactic information.

The table below gives an overview of the morphological ANNOT Language Objects available in the Chinese Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation used in TLML syntax
`ACTOR_员.ANNOT`	`ACTOR_YUAN`
`ACTOR_师.ANNOT`	`ACTOR_SHI`
`ACTOR_者.ANNOT`	`ACTOR_ZHE`
`ASPECT_PERFECTIVE.ANNOT`	`ASPECT_PERFECTIVE`
`ASPECT_了.ANNOT`	`ASPECT_PERFECTIVE_LE`
`ASPECT_在.ANNOT`	`ASPECT_PREVERBAL_PROGRESSIVE_ZAI`
`ASPECT_正在.ANNOT`	`ASPECT_PREVERBAL_PROGRESSIVE_ZHENGZAI`
`ASPECT_着.ANNOT`	`ASPECT_PROGRESSIVE_ZHE`
`ASPECT_过.ANNOT`	`ASPECT_EXPERIENTIAL_GUO`
`CITY_城.ANNOT`	`CITY_CHENG`
`CITY_市.ANNOT`	`CITY_SHI`
`COMPARATIVE_于.ANNOT`	`COMPARATIVE_YU`
`DIR_上.ANNOT`	`DIR_NONDEICTIC_SHANG`
`DIR_下.ANNOT`	`DIR_NONDEICTIC_XIA`
`DIR_出.ANNOT`	`DIR_NONDEICTIC_CHU`
`DIR_去.ANNOT`	`DIR_DEICTIC_QU`
`DIR_回.ANNOT`	`DIR_NONDEICTIC_HUI`
`DIR_开.ANNOT`	`DIR_NONDEICTIC_KAI`
`DIR_来.ANNOT`	`DIR_DEICTIC_LAI`
`DIR_起.ANNOT`	`DIR_NONDEICTIC_QI`
`DIR_过.ANNOT`	`DIR_NONDEICTIC_GUO`
`DIR_进.ANNOT`	`DIR_NONDEICTIC_JIN`
`ERHUA_儿.ANNOT`	`RCOLORING_ERHUA`
`LOC_上.ANNOT`	`LOC_ON_SHANG`
`LOC_下.ANNOT`	`LOC_UNDER_XIA`
`LOC_中.ANNOT`	`LOC_IN_ZHONG`
`LOC_内.ANNOT`	`LOC_INSIDE_NEI`
`LOC_前.ANNOT`	`LOC_BEFORE_QIAN`
`LOC_后.ANNOT`	`LOC_BEHIND_HOU`
`LOC_外.ANNOT`	`LOC_OUTSIDE_WAI`
`LOC_旁.ANNOT`	`LOC_NEXTTO_PANG`
`LOC_里.ANNOT`	`LOC_INSIDE_LI`
`MODE_IMPERATIVE.ANNOT`	`MODE_IMPERATIVE`
`MST_ASPECT.ANNOT`	`ASPECT.POS`
`MST_LOCALIZER.ANNOT`	`LOC.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RESULTATIVE.ANNOT`	`RESULTATIVE.POS`
`MST_TEMPORAL.ANNOT`	`TEMPORAL.POS`
`NEG_不.ANNOT`	`NEG_BU`
`NEG_别.ANNOT`	`NEG_BIE`
`NEG_否.ANNOT`	`NEG_FOU`
`NEG_没.ANNOT`	`NEG_MEI`
`NEG_没有.ANNOT`	`NEG_MEIYOU`
`PLURAL_们.ANNOT`	`PLURAL_MEN`
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_FOREIGN.ANNOT`	`FOREIGN.POS`
`POS_FW.ANNOT`	`BA.POS`, `CC.POS`, `CS.POS`, `DEC.POS`, `DEG.POS`, `DET.POS`, `ETC.POS`, `FOREIGN.POS`, `INTERJ.POS`, `LB.POS`, `MANNER.POS`, `MSP.POS`, `ONOMAT.POS`, `PARTICLE.POS`, `PREP.POS`, `SB.POS`, `SENTFINP.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_MEASURE.ANNOT`	`MEASURE.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_ORDINAL.ANNOT`	`ORDINAL.POS`
`POS_PARTICLE.ANNOT`	`PARTICLE.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_VC.ANNOT`	`VC.POS`
`POS_VE.ANNOT`	`VE.POS`
`POS_VERB.ANNOT`	`VB.POS`
`POS_VV.ANNOT`	`VV.POS`
`PROPERTY_度.ANNOT`	`PROPERTY_DU`
`PROPERTY_性.ANNOT`	`PROPERTY_XING`
`PROVINCE_省.ANNOT`	`PROVINCE_SHENG`
`REDUP.ANNOT`	`REDUP`
`RESULT_住.ANNOT`	`RESULT_ZHU`
`RESULT_到.ANNOT`	`RESULT_DAO`
`RESULT_好.ANNOT`	`RESULT_HAO`
`RESULT_完.ANNOT`	`RESULT_WAN`
`RESULT_成.ANNOT`	`RESULT_CHENG`
`RESULT_掉.ANNOT`	`RESULT_DIAO`
`RESULT_起.ANNOT`	`RESULT_QI`
`SPLIT_上台.ANNOT`	`SPLIT_SHANGTAI`
`SPLIT_上当.ANNOT`	`SPLIT_SHANGDANG`
`SPLIT_下台.ANNOT`	`SPLIT_XIATAI`
`SPLIT_丢人.ANNOT`	`SPLIT_DIUREN`
`SPLIT_争气.ANNOT`	`SPLIT_ZHENGQI`
`SPLIT_交手.ANNOT`	`SPLIT_JIAOSHOU`
`SPLIT_伤人.ANNOT`	`SPLIT_SHANGREN`
`SPLIT_伤心.ANNOT`	`SPLIT_SHANGXIN`
`SPLIT_伸手.ANNOT`	`SPLIT_SHENSHOU`
`SPLIT_住院.ANNOT`	`SPLIT_ZHUYUAN`
`SPLIT_作战.ANNOT`	`SPLIT_ZUOZHAN`
`SPLIT_作文.ANNOT`	`SPLIT_ZUOWEN`
`SPLIT_作案.ANNOT`	`SPLIT_ZUOAN`
`SPLIT_做客.ANNOT`	`SPLIT_ZUOKE`
`SPLIT_像样.ANNOT`	`SPLIT_XIANGYANG`
`SPLIT_入学.ANNOT`	`SPLIT_RUXUE`
`SPLIT_减产.ANNOT`	`SPLIT_JIANCHAN`
`SPLIT_出名.ANNOT`	`SPLIT_CHUMING`
`SPLIT_出差.ANNOT`	`SPLIT_CHUCHAI`
`SPLIT_出神.ANNOT`	`SPLIT_CHUSHEN`
`SPLIT_出门.ANNOT`	`SPLIT_CHUMEN`
`SPLIT_出面.ANNOT`	`SPLIT_CHUMIAN`
`SPLIT_分红.ANNOT`	`SPLIT_FENHONG`
`SPLIT_到期.ANNOT`	`SPLIT_DAOQI`
`SPLIT_办公.ANNOT`	`SPLIT_BANGONG`
`SPLIT_办学.ANNOT`	`SPLIT_BANXUE`
`SPLIT_加工.ANNOT`	`SPLIT_JIAGONG`
`SPLIT_加油.ANNOT`	`SPLIT_JIAYOU`
`SPLIT_动身.ANNOT`	`SPLIT_DONGSHEN`
`SPLIT_劳驾.ANNOT`	`SPLIT_LAOJIA`
`SPLIT_升学.ANNOT`	`SPLIT_SHENGXUE`
`SPLIT_及格.ANNOT`	`SPLIT_JIGE`
`SPLIT_发炎.ANNOT`	`SPLIT_FAYAN`
`SPLIT_叹气.ANNOT`	`SPLIT_TANQI`
`SPLIT_吃亏.ANNOT`	`SPLIT_CHIKUI`
`SPLIT_吃惊.ANNOT`	`SPLIT_CHIJING`
`SPLIT_吃苦.ANNOT`	`SPLIT_CHIKU`
`SPLIT_听话.ANNOT`	`SPLIT_TINGHUA`
`SPLIT_吵嘴.ANNOT`	`SPLIT_CHAOZUI`
`SPLIT_吵架.ANNOT`	`SPLIT_CHAOJIA`
`SPLIT_告状.ANNOT`	`SPLIT_GAOZHUANG`
`SPLIT_埋头.ANNOT`	`SPLIT_MAITOU`
`SPLIT_增产.ANNOT`	`SPLIT_ZENGCHAN`
`SPLIT_失业.ANNOT`	`SPLIT_SHIYE`
`SPLIT_安心.ANNOT`	`SPLIT_ANXIN`
`SPLIT_定性.ANNOT`	`SPLIT_DINGXING`
`SPLIT_宣誓.ANNOT`	`SPLIT_XUANSHI`
`SPLIT_对头.ANNOT`	`SPLIT_DUITOU`
`SPLIT_就业.ANNOT`	`SPLIT_JIUYE`
`SPLIT_帮忙.ANNOT`	`SPLIT_BANGMANG`
`SPLIT_干杯.ANNOT`	`SPLIT_GANBEI`
`SPLIT_幽默.ANNOT`	`SPLIT_YOUMO`
`SPLIT_延期.ANNOT`	`SPLIT_YANQI`
`SPLIT_开幕.ANNOT`	`SPLIT_KAIMU`
`SPLIT_开课.ANNOT`	`SPLIT_KAIKE`
`SPLIT_当面.ANNOT`	`SPLIT_DANGMIAN`
`SPLIT_待业.ANNOT`	`SPLIT_DAIYE`
`SPLIT_念书.ANNOT`	`SPLIT_NIANSHU`
`SPLIT_懂事.ANNOT`	`SPLIT_DONGSHI`
`SPLIT_成套.ANNOT`	`SPLIT_CHENGTAO`
`SPLIT_打架.ANNOT`	`SPLIT_DAJIA`
`SPLIT_打猎.ANNOT`	`SPLIT_DALIE`
`SPLIT_打针.ANNOT`	`SPLIT_DAZHEN`
`SPLIT_执勤.ANNOT`	`SPLIT_ZHIQIN`
`SPLIT_执政.ANNOT`	`SPLIT_ZHIZHENG`
`SPLIT_把关.ANNOT`	`SPLIT_BAGUAN`
`SPLIT_投产.ANNOT`	`SPLIT_TOUCHAN`
`SPLIT_投标.ANNOT`	`SPLIT_TOUBIAO`
`SPLIT_投资.ANNOT`	`SPLIT_TOUZI`
`SPLIT_报道.ANNOT`	`SPLIT_BAODAO`
`SPLIT_拐弯.ANNOT`	`SPLIT_GUAIWAN`
`SPLIT_招手.ANNOT`	`SPLIT_ZHAOSHOU`
`SPLIT_招生.ANNOT`	`SPLIT_ZHAOSHENG`
`SPLIT_拜年.ANNOT`	`SPLIT_BAINIAN`
`SPLIT_挂号.ANNOT`	`SPLIT_GUAHAO`
`SPLIT_捣蛋.ANNOT`	`SPLIT_DAODAN`
`SPLIT_排队.ANNOT`	`SPLIT_PAIDUI`
`SPLIT_接班.ANNOT`	`SPLIT_JIEBAN`
`SPLIT_提名.ANNOT`	`SPLIT_TIMING`
`SPLIT_提醒.ANNOT`	`SPLIT_TIXING`
`SPLIT_搞鬼.ANNOT`	`SPLIT_GAOGUI`
`SPLIT_摄影.ANNOT`	`SPLIT_SHEYING`
`SPLIT_操心.ANNOT`	`SPLIT_CAOXIN`
`SPLIT_放假.ANNOT`	`SPLIT_FANGJIA`
`SPLIT_放手.ANNOT`	`SPLIT_FANGSHOU`
`SPLIT_散步.ANNOT`	`SPLIT_SANBU`
`SPLIT_敬礼.ANNOT`	`SPLIT_JINGLI`
`SPLIT_施工.ANNOT`	`SPLIT_SHIGONG`
`SPLIT_毕业.ANNOT`	`SPLIT_BIYE`
`SPLIT_沾光.ANNOT`	`SPLIT_ZHANGUANG`
`SPLIT_泄气.ANNOT`	`SPLIT_XIEQI`
`SPLIT_注册.ANNOT`	`SPLIT_ZHUCE`
`SPLIT_洗澡.ANNOT`	`SPLIT_XIZAO`
`SPLIT_照相.ANNOT`	`SPLIT_ZHAOXIANG`
`SPLIT_献身.ANNOT`	`SPLIT_XIANSHEN`
`SPLIT_理发.ANNOT`	`SPLIT_LIFA`
`SPLIT_生效.ANNOT`	`SPLIT_SHENGXIAO`
`SPLIT_生气.ANNOT`	`SPLIT_SHENGQI`
`SPLIT_用功.ANNOT`	`SPLIT_YONGGONG`
`SPLIT_留意.ANNOT`	`SPLIT_LIUYI`
`SPLIT_着急.ANNOT`	`SPLIT_ZHEJI`
`SPLIT_睡觉.ANNOT`	`SPLIT_SHUIJUE`
`SPLIT_矿工.ANNOT`	`SPLIT_KUANGGONG`
`SPLIT_种地.ANNOT`	`SPLIT_ZHONGDI`
`SPLIT_称心.ANNOT`	`SPLIT_CHENGXIN`
`SPLIT_移民.ANNOT`	`SPLIT_YIMIN`
`SPLIT_算数.ANNOT`	`SPLIT_SUANSHU`
`SPLIT_纳闷.ANNOT`	`SPLIT_NAMEN`
`SPLIT_结婚.ANNOT`	`SPLIT_JIEHUN`
`SPLIT_结果.ANNOT`	`SPLIT_JIEGUO`
`SPLIT_绝望.ANNOT`	`SPLIT_JUEWANG`
`SPLIT_罢工.ANNOT`	`SPLIT_BAGONG`
`SPLIT_致电.ANNOT`	`SPLIT_ZHIDIAN`
`SPLIT_行军.ANNOT`	`SPLIT_XINGJUN`
`SPLIT_行贿.ANNOT`	`SPLIT_XINGHUI`
`SPLIT_见面.ANNOT`	`SPLIT_JIANMIAN`
`SPLIT_请假.ANNOT`	`SPLIT_QINGJIA`
`SPLIT_走路.ANNOT`	`SPLIT_ZOULU`
`SPLIT_起哄.ANNOT`	`SPLIT_QIHONG`
`SPLIT_起床.ANNOT`	`SPLIT_QICHUANG`
`SPLIT_起草.ANNOT`	`SPLIT_QICAO`
`SPLIT_起身.ANNOT`	`SPLIT_QISHEN`
`SPLIT_跑步.ANNOT`	`SPLIT_PAOBU`
`SPLIT_跳舞.ANNOT`	`SPLIT_TIAOWU`
`SPLIT_辞职.ANNOT`	`SPLIT_CIZHI`
`SPLIT_迎面.ANNOT`	`SPLIT_YINGMIAN`
`SPLIT_还原.ANNOT`	`SPLIT_HUANYUAN`
`SPLIT_违法.ANNOT`	`SPLIT_WEIFA`
`SPLIT_送行.ANNOT`	`SPLIT_SONGXING`
`SPLIT_造反.ANNOT`	`SPLIT_ZAOFAN`
`SPLIT_遭殃.ANNOT`	`SPLIT_ZAOYANG`
`SPLIT_配套.ANNOT`	`SPLIT_PEITAO`
`SPLIT_闭幕.ANNOT`	`SPLIT_BIMU`
`SPLIT_问好.ANNOT`	`SPLIT_WENHAO`
`SPLIT_随便.ANNOT`	`SPLIT_SUIBIAN`
`SPLIT_集资.ANNOT`	`SPLIT_JIZI`
`SPLIT_集邮.ANNOT`	`SPLIT_JIYOU`
`SPLIT_鞠躬.ANNOT`	`SPLIT_JUGONG`
`SPLIT_鼓掌.ANNOT`	`SPLIT_GUZHANG`
`TRANSFORM_化.ANNOT`	`TRANSFORM_HUA`
`VB不VB.ANNOT`	`VNOTV_BU`
`VB否VB.ANNOT`	`VNOTV_FOU`
`VB没VB.ANNOT`	`VNOTV_MEI`

Danish

The morphological annotations are generated by the Danish POS Tagger and Morphological Analyzer input processor, which uses a machine learnt statistical model to tag every word in the user-input with exactly one tag from the OPEN NLP and Center for Sprogteknologi (CST) tag sets. It then maps these tags to the Teneo POS annotations like: NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

The Danish Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number and verb tense.

The table below shows the morphological ANNOT Language Objects available in the Danish Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation(s) used in TLML Syntax
`POS_ADJ.ANNOT`	`ADJ.POS`
`POS_ADV.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_FW.ANNOT`	`CC.POS`, `CS.POS`, `DET.POS`, `INTERJ.POS`, `MISC.POS`, `PREP.POS`, `PRON.POS`, `POSS.POS`
`POS_INTERROG.ANNOT`	`INTERROG.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_ORDINAL.ANNOT`	`ORDINAL.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`, `HYPHEN.POS`
`MST_ACTIVE.ANNOT`	`ACTIVE.POS`
`MST_COMPARATIVE.ANNOT`	`COMPARATIVE.POS`
`MST_DEFINITE.ANNOT`	`DEF.POS`
`MST_INDEFINITE.ANNOT`	`INDEF.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`
`MST_PASSIVE.ANNOT`	`PASSIVE.POS`
`MST_PAST.ANNOT`	`PAST.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_POSITIVE.ANNOT`	`POSITIVE.POS`
`MST_POSSESSIVE.ANNOT`	`POSS.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RELATIVE.ANNOT`	`REL.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUPERLATIVE.ANNOT`	`SUPERLATIVE.POS`
`MST_SUPINE.ANNOT`	`SUPINE.POS`

Dutch

Morphological annotations are generated by the Dutch POS Tagger and Morphological Analyzer input processor, which uses a machine learnt statistical model to tag every word in the user-input with exactly one tag form the adaptation of the German STTS tag set. It then maps these tags to Teneo POS annotations like: NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

The Dutch Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number or verb tense.

The table below gives an overview of the morphological ANNOT Language Objects implemented in the Dutch Lexical Resource.

ANNOT language object	Annotation(s) used in TLML Syntax
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_DETERMINER.ANNOT`	`DET.POS`
`POS_FW.ANNOT`	`CC.POS`, `CS.POS`, `DET.POS`, `INTERJ.POS`, `MISC.POS`, `PARTICLE.POS` , `PREP.POS`, `POST.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_TE.ANNOT`	`TE.POS`
`POS_VERB.ANNOT`	`VB.POS`
`POS_VERBPREFIX.ANNOT`	`VBPREFIX.POS`
`MST_1STPERSON.ANNOT`	`1STPERSON.POS`
`MST_2NDPERSON.ANNOT`	`2NDPERSON.POS`
`MST_3RDPERSON.ANNOT`	`3RDPERSON.POS`
`MST_ATTRIBUTIVE.ANNOT`	`ATTRIB.POS`
`MST_COMPARATIVE.ANNOT`	`COMPARATIVE.POS`
`MST_DEMONSTRATIVE.ANNOT`	`DEMOS.POS`
`MST_FINITE.ANNOT`	`FINITE.POS`
`MST_IMPERATIVE.ANNOT`	`IMP.POS`
`MST_INDEFINITE.ANNOT`	`INDEF.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_INTERROGATIVE.ANNOT`	`INTERROG.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`
`MST_PAST.ANNOT`	`PAST.POS`
`MST_PERSONAL.ANNOT`	`PERS.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_POSITIVE.ANNOT`	`POSITIVE.POS`
`MST_POSSESSIVE.ANNOT`	`POSS.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RELATIVE.ANNOT`	`REL.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUBSTITUTIVE.ANNOT`	`SUBST.POS`
`MST_SUPERLATIVE.ANNOT`	`SUPERLATIVE.POS`

Person and number annotations for verbs

The Dutch Part-of-Speech Tagger input processor also annotates morphological information about the person and the number for all finite verb forms in an additional rule-based step.

The corresponding Teneo POS annotations are presented in the below table.

`Person`	`Number`
`1STPERSON.POS`	`SG.POS`
`2NDPERSON.POS 3RDPERSON.POS`	`PL.POS`

English

Morphological annotations are generated by the English POS Tagger and Morphological Analyzer input processor, which uses a machine learnt statistical model to tag every word in the user-input with exactly one tag from the Penn Treebank tag set. It then maps these tags to Teneo POS annotations like NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

The English Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number and verb tense.

The table below gives an overview of the morphological ANNOT Language Objects available in the English Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation(s) used in TLML Syntax
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_BRACKET.ANNOT`	`BRACKET.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_POSSESSIVE.ANNOT`	`POSS.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_VERB.ANNOT`	`VB.POS`
`POS_INTERROG.ANNOT`	`INTERROG.POS`
`POS_FW.ANNOT`	`CC.POS`, `DET.POS`, `EX.POS`, `FOREIGN.POS`, `PREP.POS`, `LS.POS`, `PREDET.POS`, `PARTICLE.POS`, `SYM.POS`, `INTERJ.POS`, `POSS.POS`
`MST_3RDPERSON.ANNOT`	`3RDPERSON.POS`
`MST_COMPARATIVE.ANNOT`	`COMP.POS`
`MST_GERUND.ANNOT`	`GERUND.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`
`MST_PAST.ANNOT`	`PAST.POS`
`MST_PERSONAL.ANNOT`	`PERS.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUPERLATIVE.ANNOT`	`SUPER.POS`

French

Morphological annotations are generated by the French POS Tagger and Morphological Analyzer input processor, which uses a machine learnt statistical model to tag every word in the user-input with exactly one tag from the French Treebank tag set. It then maps these tags to Teneo POS annotations like: NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

The French Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number or verb tense.

The table below gives an overview of the morphological ANNOT Language Objects available in the French Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation(s) used in TLML Syntax
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_FOREIGN.ANNOT`	`FOREIGN.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_PREFIX.ANNOT`	`PREFIX.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_VERB.ANNOT`	`VB.POS`
`POS_FW.ANNOT`	`CC.POS`, `CS.POS`, `DET.POS`, `INTERJ.POS`, `MISC.POS`, `PREP.POS`
`MST_1STPERSON.ANNOT`	`1STPERSON.POS`
`MST_2NDPERSON.ANNOT`	`2NDPERSON.POS`
`MST_3RDPERSON.ANNOT`	`3RDPERSON.POS`
`MST_CLITIC.ANNOT`	`CLITIC.POS`
`MST_CONDITIONAL.ANNOT`	`CONDITIONAL.POS`
`MST_DEMONSTRATIVE.ANNOT`	`DEMOS.POS`
`MST_EXCLAMATIVE.ANNOT`	`EXCLAM.POS`
`MST_FUTURE.ANNOT`	`FUTURE.POS`
`MST_IMPERATIVE.ANNOT`	`IMP.POS`
`MST_IMPERFECT.ANNOT`	`IMPERFECT.POS`
`MST_INDEFINITE.ANNOT`	`INDEF.POS`
`MST_INDICATIVE.ANNOT`	`INDICATIVE.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_INTERROGATIVE.ANNOT`	`INTERROG.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`
`MST_NEGATION.ANNOT`	`NEGATION.POS`
`MST_ORDINAL.ANNOT`	`ORDINAL.POS`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`
`MST_PAST.ANNOT`	`PERFECT.POS`, `IMPERFECT.POS`, `PARTICIPLE.POS`
`MST_PERFECT.ANNOT`	`PERFECT.POS`
`MST_PERSONAL.ANNOT`	`PERS.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_POSSESSIVE.ANNOT`	`POSS.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RELATIVE.ANNOT`	`REL.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUBJUNCTIVE.ANNOT`	`SUBJUNCTIVE.POS`

German

Morphological annotations are generated by the German POS Tagger and Morphological Analyzer input processor, which is a hybrid input processor that is first using a machine learnt statistical model to tag every word in the user-input with exactly one tag from an extended version of the STTS tag set. It then maps these tags to Teneo POS annotations like NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

After mapping the tags to Teneo annotations, the German POS Tagger and Morphological Analyzer input processor uses this information and some cleverly designed rules to further distinguish the person, i.e. 1STPERSON.POS, 2NDPERSON.POS or 3RDPERSON.POS, and the number, i.e. SG.POS or PL.POS, for every verb in the user input, thus providing the complete range of morphological information to the user. For imperatives (VVIMP), only information about the number is annotation. For ambiguous cases like Kommen Sie mit! both SG.POS and PL.POS are annotated.

The German Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic morpho-syntactic traits such as number or verb tense.

The table below gives and overview of the morphological ANNOT Language Objects available in the German Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation(s) used in TLML Syntax	Notes
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`	Matches with all forms of adjectives, i.e. attributive an predicative forms as well as adjectives that are used adverbially (as in “Er rennt schnell”). Additionally, past participles that act as an adjective (as in “Das vermisste Kind”) and all present participles are annotated as adjectives.
`POS_ADPOSITION.ANNOT`	`ADPOSITION.POS`	`Matches with prepositions, postpositions and circum-positions.`
`POS_ADVERB.ANNOT`	`ADV.POS`	Matches with pure adverbs, i.e. adverbs that are not directly derived from adjectives.
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_CONJUNCTION.ANNOT`	`CC.POS`, `CS.POS`	Matches with coordinating as well as subordinating conjunctions.
`POS_DETERMINER.ANNOT`	`DET.POS`	Matches with definite and indefinite articles as well as demonstrative pronouns.
`POS_FW.ANNOT`	`ADPOSITION.POS`, `CC.POS`, `CS.POS`, `DET.POS`, `PARTICLE.POS`, `INTERJ.POS`, `POSS.POS`, `TRUNC.POS`, `XY.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_NOUN.ANNOT`	`NN.POS`	Matches with normal nouns and proper nouns.
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_VERB.ANNOT`	`VB.POS`
`POS_VERBPREFIX.ANNOT`	`VBPREFIX.POS`	Matches with a verb prefix that is separated from the rest of the verb (as in “Der Zug kommt um vier Uhr an”).
`POS_ZU.ANNOT`	`ZU.POS`	Matches with “zu” if it is used with an infinitive (as in “Ich freue mich hier zu sein”). Other forms of “zu” are annotated as prepositions (as in “Er kommt zu mir”) or as adverbs in combination with adjectives (as in “Das ist zu teuer”).
`MST_1STPERSON.ANNOT`	`1STPERSON.POS`
`MST_2NDPERSON.ANNOT`	`2NDPERSON.POS`
`MST_3RDPERSON.ANNOT`	`3RDPERSON.POS`
`MST_ATTRIBUTIVE.ANNOT`	`ATTRIB.POS`	Only used together with interrogative pronouns that are used attributively (as in “Welche Farbe willst du?” as opposed to substituting interrogative pronouns (as in “Welche willst du?).
`MST_COMPARATIVE.ANNOT`	`COMPARATIVE.POS`
`MST_FINITE.ANNOT`	`FINITE.POS`
`MST_INDEFINITE.ANNOT`	`INDEF.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_IMPERATIVE.ANNOT`	`IMP.POS`
`MST_INTERROGATIVE.ANNOT`	`INTERROG.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`	Matches exclusively with any form of the verbs “können”, “müssen”, “dürfen”, “wollen”, “sollen” or “mögen” regardless whether they are used together with an infinitive in the context.
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`	Only matches with past participles. Present participles are annotated as adjectives.
`MST_PAST.ANNOT`	`PAST.POS`
`MST_PERSONAL.ANNOT`	`PERS.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_POSITIVE.ANNOT`	`POSITIVE.POS`
`MST_POSSESSIVE.ANNOT`	`POSS.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RELATIVE.ANNOT`	`REL.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUBSITITUTIVE.ANNOT`	`SUBST.POS`	Only used together with interrogative pronouns that are used substitutively (as in “Welche willst du?”) as opposed to attributive interrogative pronouns (as in “Welche Farbe willst du?).
`MST_SUPERLATIVE.ANNOT`	`SUPERLATIVE.POS`

Italian

Morphological annotations are generated by the Italian POS Tagger and Morphological Analyzer input processor, which uses a machine learnt statistical model to tag every word in the user-input with exactly one tag from the modified TANL tag set. In the final tag set that was used to train the Italian POS Tagger, the tag set for main verbs (V) and those for auxiliary verbs (VA) were merged, and the tags for all determiners (DD, DE, DI, DQ, DR), the pre-determiner tag (T) and the tags for articles (RI, RD) were merged into one single DET tag. The input processor next maps these tags to Teneo POS annotations like: NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

The Italian Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number and verb tense.

The table below gives an overview of the morphological ANNOT Language Objects available in the Italian Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation(s) used in TLML Syntax
`POS_ABBREVIATION.ANNOT`	`ABBREV.POS`
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_FOREIGN.ANNOT`	`FOREIGN.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_ORDINAL.ANNOT`	`ORDINAL.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_HASHTAG.ANNOT`	`HASHTAG.POS`
`POS_ATMENTION.ANNOT`	`ATMENTION.POS`
`POS_EMOTICON.ANNOT`	`EMOTICON.POS`
`POS_VERB.ANNOT`	`VB.POS`
`POS_FW.ANNOT`	`CC.POS`, `CS.POS`, `DET.POS`, `INTERJ.POS`, `MISC.POS`, `PREP.POS`
`MST_3RDPERSON.ANNOT`	`3RDPERSON.POS`
`MST_CLITIC.ANNOT`	`CLITIC.POS`
`MST_CONDITIONAL.ANNOT`	`CONDITIONAL.POS`
`MST_DEMONSTRATIVE.ANNOT`	`DEMOS.POS`
`MST_FUTURE.ANNOT`	`FUTURE.POS`
`MST_GERUND.ANNOT`	`GERUND.POS`
`MST_INDEFINITE.ANNOT`	`INDEF.POS`
`MST_INDICATIVE.ANNOT`	`INDICATIVE.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_IMPERATIVE.ANNOT`	`IMP.POS`
`MST_IMPERFECT.ANNOT`	`IMPERFECT.POS`
`MST_INTERROGATIVE.ANNOT`	`INTERROG.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`
`MST_NEGATION.ANNOT`	`NEGATION.POS`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`
`MST_PAST.ANNOT`	`PERFECT.POS`, `IMPERFECT.POS`, `PARTICIPLE.POS`
`MST_PERFECT.ANNOT`	`PERFECT.POS`
`MST_PERSONAL.ANNOT`	`PERS.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_POSSESSIVE.ANNOT`	`POSS.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RELATIVE.ANNOT`	`REL.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUBJUNCTIVE.ANNOT`	`SUBJUNCTIVE.POS`

Japanese

The Japanese Annotator input processor uses a state-of-the-art tokenizer and morphological analyzer to split the user inputs into tokens and annotate them with morphological and syntactic information.

The table below gives an overview of the morphological ANNOT Language Objects available in the Japanese Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation used in TLML Syntax
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_CONJUNCTION.ANNOT`	`CONJ.POS`
`POS_COPULA.POS`	`COPULA.POS`
`POS_COUNTER.ANNOT`	`COUNTER.POS`
`POS_DETERMINER.ANNOT`	`DET.POS`
`POS_FW.ANNOT`	`FW.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_PARTICLE.ANNOT`	`PARTICLE.POS`
`POS_PREFIX.POS`	`PREFIX.POS`
`POS_PREPOSITION.ANNOT`	`PREP.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PROPER.ANNOT`	`PROPER.POS`
`POS_SUFFIX.ANNOT`	`SUFFIX.POS`
`POS_SYMBOL.ANNOT`	`SYM.POS`
`POS_VERB.ANNOT`	`VB.POS`
`MST_ALMOST.ANNOT`	`ALMOST.MST`
`MST_ASSUMPTION.ANNOT`	`ASSUMPTION.MST`
`MST_CAUSATIVE.ANNOT`	`CAUSATIVE.MST`
`MST_だす.ANNOT`	`DASU.MST`
`MST_DESIRE.ANNOT`	`DESIRE.MST`
`MST_EXCESS.ANNOT`	`EXCESS.MST`
`MST_FORMAL.ANNOT`	`FORMAL.MST`
`MST_がる.ANNOT`	`GARU.MST`
`MST_GERUND.ANNOT`	`GERUND.MST`
`MST_はじめる.ANNOT`	`HAJIMERU.MST`
`MST_IMPERATIVE.ANNOT`	`IMPERATIVE.MST`
`MST_ITERATIVE.ANNOT`	`ITERATIVE.MST`
`MST_かねる.ANNOT`	`KANERU.MST`
`MST_きる.ANNOT`	`KIRU.MST`
`MST_NEGATION.ANNOT`	`NEGATION.MST`
`MST_おえる.ANNOT`	`OERU.MST`
`MST_おわる.ANNOT`	`OWARU.MST`
`MST_PASSIVE.ANNOT`	`PASSIVE.MST`
`MST_PAST.ANNOT`	`PAST.MST`
`MST_PROGRESSIVE.ANNOT`	`PROGRESSIVE.MST`
`MST_連用形.ANNOT`	`RENYOKEI.MST`
`MST_てあげる.ANNOT`	`TEAGERU.MST`
`MST_ていく.ANNOT`	`TEIKU.MST`
`MST_ていただく.ANNOT`	`TEITADAKU.MST`
`MST_てくださる.ANNOT`	`TEKUDASARU.MST`
`MST_てくれる.ANNOT`	`TEKURERU.MST`
`MST_てみる.ANNOT`	`TEMIRU.MST`
`MST_てもらう.ANNOT`	`TEMORAU.MST`
`MST_ておく.ANNOT`	`TEOKU.MST`
`MST_てしまう.ANNOT`	`TESHIMAU.MST`
`MST_てやる.ANNOT`	`TEYARU.MST`
`MST_VOLITION.ANNOT`	`VOLITION.MST`
`MST_やがる.ANNOT`	`YAGARU.MST`
`MST_NONNEG.ANNOT`	`NONNEG.MST`
`MST_NONPAST.ANNOT`	`NONPAST.MST`

Spanish

Morphological annotations are generated by the Spanish POS Tagger and Morphological Analyzer input processor, which uses a machine learnt statistical model to tag every word in the user-input with exactly one tag from the EAGLES Guidelines tag set. It then maps these tags to Teneo POS annotations like: NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

The Spanish Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number and verb tense.

The table below gives an overview of the morphological ANNOT Language Objects available in the Spanish Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation(s) used in TLML Syntax
`POS_ABBREVIATION.ANNOT`	`ABBREV.POS`
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_FOREIGN.ANNOT`	`FOREIGN.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_ORDINAL.ANNOT`	`ORDINAL.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_VERB.ANNOT`	`VB.POS`
`POS_FW.ANNOT`	`CC.POS`, `CS.POS`, `DET.POS`, `INTERJ.POS`, `MISC.POS`, `PREP.POS`
`MST_1STPERSON.ANNOT`	`1STPERSON.POS`
`MST_2NDPERSON.ANNOT`	`2NDPERSON.POS`
`MST_3RDPERSON.ANNOT`	`3RDPERSON.POS`
`MST_CLITIC.ANNOT`	`CLITIC.POS`
`MST_CONDITIONAL.ANNOT`	`CONDITIONAL.POS`
`MST_DEMONSTRATIVE.ANNOT`	`DEMOS.POS`
`MST_FUTURE.ANNOT`	`FUTURE.POS`
`MST_GERUND.ANNOT`	`GERUND.POS`
`MST_IMPERATIVE.ANNOT`	`IMP.POS`
`MST_IMPERFECT.ANNOT`	`IMPERFECT.POS`
`MST_INDEFINITE.ANNOT`	`INDEF.POS`
`MST_INDICATIVE.ANNOT`	`INDICATIVE.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_INTERROGATIVE.ANNOT`	`INTERROG.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`
`MST_NEGATION.ANNOT`	`NEGATION.POS`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`
`MST_PAST.ANNOT`	`PERFECT.POS`, `IMPERFECT.POS`, `PARTICIPLE.POS`
`MST_PERFECT.ANNOT`	`PERFECT.POS`
`MST_PERSONAL.ANNOT`	`PERS.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_POSSESSIVE.ANNOT`	`POSS.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RELATIVE.ANNOT`	`REL.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUBJUNCTIVE.ANNOT`	`SUBJUNCTIVE.POS`

Swedish

Morphological annotations are generated by the Swedish POS Tagger and Morphological Analyzer input processor, which uses a machine learnt statistical model to tag every word in the user-input with exactly one tag from the Stockholm-Umeå corpus tag set. It then maps these tags to Teneo POS annotations like: NN.POS, VP.POS, PAST.POS, PRESENT.POS, etc.

The Swedish Teneo NLU Ontology and Semantic Network distinguishes morphological ANNOT Language Objects between POS Language Objects, which are meant to capture part of speech such as verb and noun; and MST Language Objects, which are meant to capture morpho-syntactic traits such as noun number or verb tense.

The table below gives an overview of the morphological ANNOT Language Objects available in the Swedish Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation(s) used in TLML Syntax
`POS_ADJ.ANNOT`	`ADJ.POS`
`POS_ADV.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_FOREIGN.ANNOT`	`FOREIGN.POS`
`POS_FW.ANNOT`	`CC.POS`, `CS.POS`, `DET.POS`, `INTERJ.POS`, `MISC.POS`, `PREP.POS`, `PARTICLE.POS`, `POSS.POS`
`POS_INTERROG.ANNOT`	`INTERROG.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_ORDINAL.ANNOT`	`ORDINAL.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`, `HYPHEN.POS`
`POS_VERB.ANNOT`	`VB.POS`
`MST_ACTIVE.ANNOT`	`ACTIVE.POS`
`MST_COMPARATIVE.ANNOT`	`COMPARATIVE.POS`
`MST_DEFINITE.ANNOT`	`DEF.POS`
`MST_INDEFINITE.ANNOT`	`INDEF.POS`
`MST_INFINITIVE.ANNOT`	`INF.POS`
`MST_MODAL.ANNOT`	`MODAL.POS`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.POS`
`MST_PASSIVE.ANNOT`	`PASSIVE.POS`
`MST_PAST.ANNOT`	`PAST.POS`
`MST_PERSONAL.ANNOT`	`PERS.POS`
`MST_PLURAL.ANNOT`	`PL.POS`
`MST_POSITIVE.ANNOT`	`POSITIVE.POS`
`MST_POSSESSIVE.ANNOT`	`POSS.POS`
`MST_PRESENT.ANNOT`	`PRESENT.POS`
`MST_PROPER.ANNOT`	`PROPER.POS`
`MST_RELATIVE.ANNOT`	`REL.POS`
`MST_SINGULAR.ANNOT`	`SG.POS`
`MST_SUPERLATIVE.ANNOT`	`SUPERLATIVE.POS`
`MST_SUPINE.ANNOT`	`SUPINE.POS`

Turkish

The Turkish Analyzer input processor uses a state-of-the-art tokenizer and morphological analyzer to split the user inputs into tokens and annotate them with morphological and syntactic information.

The table below gives an overview of the morphological ANNOT Language Objects available in the Turkish Teneo NLU Ontology and Semantic Network.

Morphological ANNOT language object	Annotation used in TLML Syntax
`POS_ADJECTIVE.ANNOT`	`ADJ.POS`
`POS_ADVERB.ANNOT`	`ADV.POS`
`POS_CARDINAL.ANNOT`	`CARDINAL.POS`
`POS_CONJUNCTION.ANNOT`	`CC.POS`
`POS_DETERMINER.ANNOT`	`DET.POS`
`POS_DUPLICATOR.ANNOT`	`DUPLICATOR.POS`
`POS_INTERJECTION.ANNOT`	`INTERJ.POS`
`POS_INTERROG.ANNOT`	`INTERROG.POS`
`POS_NOUN.ANNOT`	`NN.POS`
`POS_NUMERAL.ANNOT`	`NUMERAL.POS`
`POS_ORDINAL.ANNOT`	`ORDINAL.POS`
`POS_POST_POSITIVE.ANNOT`	`POST_POSITIVE.POS`
`POS_PRONOUN.ANNOT`	`PRON.POS`
`POS_PUNCTUATION.ANNOT`	`PUNCT.POS`
`POS_VERB.ANNOT`	`VERB.POS`
`MST_1STPERSON.ANNOT`	`1STPERSON.MST`
`MST_2NDPERSON.ANNOT`	`2NDPERSON.MST`
`MST_3RDPERSON.ANNOT`	`3RDPERSON.MST`
`MST_ABILITY.ANNOT`	`ABILITY.MST`
`MST_ABLATIVE.ANNOT`	`ABLATIVE.MST`
`MST_ACCUSATIVE.ANNOT`	`ACCUSATIVE.MST`
`MST_ACQUIRE.ANNOT`	`ACQUIRE.MST`
`MST_ACT_OF.ANNOT`	`ACT_OF.MST`
`MST_ADAMANTLY.ANNOT`	`ADAMANTLY.MST`
`MST_ADJECTIVE.ANNOT`	`ADJECTIVE.MST`
`MST_ADVERB.ANNOT`	`ADVERB.MST`
`MST_AFTER_DOING_SO.ANNOT`	`AFTER_DOING_SO.MST`
`MST_AGENTIVE.ANNOT`	`AGENTIVE.MST`
`MST_ALMOST.ANNOT`	`ALMOST.MST`
`MST_AORIST.ANNOT`	`AORIST.MST`
`MST_AS_IF.ANNOT`	`AS_IF.MST`
`MST_AS_LONG_AS.ANNOT`	`AS_LONG_AS.MST`
`MST_BECOME.ANNOT`	`BECOME.MST`
`MST_BY_DOING_SO.ANNOT`	`BY_DOING_SO.MST`
`MST_CAUSATIVE.ANNOT`	`CAUSATIVE.MST`
`MST_CONDITION.ANNOT`	`CONDITION.MST`
`MST_CONJUNCTION.ANNOT`	`CONJUNCTION.MST`
`MST_COP.ANNOT`	`COP.MST`
`MST_DATIVE.ANNOT`	`DATIVE.MST`
`MST_DEMONSTRATIVE.ANNOT`	`DEMONSTRATIVE.MST`
`MST_DESIRE.ANNOT`	`DESIRE.MST`
`MST_DETERMINER.ANNOT`	`DETERMINER.MST`
`MST_DIMINUTIVE.ANNOT`	`DIMINUTIVE.MST`
`MST_DUPLICATOR.ANNOT`	`DUPLICATOR.MST`
`MST_EQUATIVE.ANNOT`	`EQUATIVE.MST`
`MST_EVER_SINCE.ANNOT`	`EVER_SINCE.MST`
`MST_FEEL_LIKE.ANNOT`	`FEEL_LIKE.MST`
`MST_FUTURE.ANNOT`	`FUTURE.MST`
`MST_GENITIVE.ANNOT`	`GENITIVE.MST`
`MST_HASTILY.ANNOT`	`HASTILY.MST`
`MST_IMPERATIVE.ANNOT`	`IMPERATIVE.MST`
`MST_INFINITIVE1.ANNOT`	`INFINITIVE1.MST`
`MST_INFINITIVE2.ANNOT`	`INFINITIVE2.MST`
`MST_INFINITIVE3.ANNOT`	`INFINITIVE3.MST`
`MST_INFORMAL.ANNOT`	`INFORMAL.MST`
`MST_INSTRUMENTAL.ANNOT`	`INSTRUMENTAL.MST`
`MST_INTERJECTION.ANNOT`	`INTERJECTION.MST`
`MST_INTERROG.ANNOT`	`INTERROG.MST`
`MST_JUST_LIKE.ANNOT`	`JUST_LIKE.MST`
`MST_LOCATIVE.ANNOT`	`LOCATIVE.MST`
`MST_LY.ANNOT`	`LY.MST`
`MST_NARRATIVE.ANNOT`	`NARRATIVE.MST`
`MST_NECESSITY.ANNOT`	`NECESSITY.MST`
`MST_NEGATIVE.ANNOT`	`NEGATIVE.MST`
`MST_NESS.ANNOT`	`NESS.MST`
`MST_NO_POSESSION.ANNOT`	`NO_POSESSION.MST`
`MST_NOMINATIVE.ANNOT`	`NOMINATIVE.MST`
`MST_NOT_STATE.ANNOT`	`NOT_STATE.MST`
`MST_NOUN.ANNOT`	`NOUN.MST`
`MST_NUMERAL.ANNOT`	`NUMERAL.MST`
`MST_OPTATIVE.ANNOT`	`OPTATIVE.MST`
`MST_PARTICIPLE.ANNOT`	`PARTICIPLE.MST`
`MST_PASSIVE.ANNOT`	`PASSIVE.MST`
`MST_PAST.ANNOT`	`PAST.MST`
`MST_PERSONAL.ANNOT`	`PERSONAL.MST`
`MST_PLURAL.ANNOT`	`PLURAL.MST`
`MST_POSSESSIVE.ANNOT`	`POSSESSIVE.MST`
`MST_POST_POSITIVE.ANNOT`	`POST_POSITIVE.MST`
`MST_PRESENT.ANNOT`	`PRESENT.MST`
`MST_PROGRESSIVE1.ANNOT`	`PROGRESSIVE1.MST`
`MST_PROGRESSIVE2.ANNOT`	`PROGRESSIVE2.MST`
`MST_PRONOUN.ANNOT`	`PRONOUN.MST`
`MST_PROPER.ANNOT`	`PROPER.MST`
`MST_PUNCTUATION.ANNOT`	`PUNCTUATION.MST`
`MST_QUANTITATIVE.ANNOT`	`QUANTITATIVE.MST`
`MST_RECIPROCAL.ANNOT`	`RECIPROCAL.MST`
`MST_REFLEXIVE.ANNOT`	`REFLEXIVE.MST`
`MST_RELATED.ANNOT`	`RELATED.MST`
`MST_RELATION.ANNOT`	`RELATION.MST`
`MST_REPEAT.ANNOT`	`REPEAT.MST`
`MST_SINCE_DOING_SO.ANNOT`	`SINCE_DOING_SO.MST`
`MST_SINGULAR.ANNOT`	`SINGULAR.MST`
`MST_START.ANNOT`	`START.MST`
`MST_STAY.ANNOT`	`STAY.MST`
`MST_UNABLE.ANNOT`	`UNABLE.MST`
`MST_VERB.ANNOT`	`VERB.MST`
`MST_WHEN.ANNOT`	`WHEN.MST`
`MST_WHILE.ANNOT`	`WHILE.MST`
`MST_WITH.ANNOT`	`WITH.MST`
`MST_WITHOUT.ANNOT`	`WITHOUT.MST`
`MST_WITHOUT_BEING_ABLE_TO_DO_SO.ANNOT`	`WITHOUT_BEING_ABLE_TO_DO_SO.MST`
`MST_WITHOUT_HAVING_DONE_SO.ANNOT`	`WITHOUT_HAVING_DONE_SO.MST`
`MST_ZERO.ANNOT`	`ZERO.MST`
`MST_ACQUIRE.ANNOT`	`ACQUIRE.MST`
`MST_ACT_OF.ANNOT`	`ACT_OF.MST`
`MST_ADAMANTLY.ANNOT`	`ADAMANTLY.MST`
`MST_ADJECTIVE.ANNOT`	`ADJECTIVE.MST`
`MST_ADVERB.ANNOT`	`ADVERB.MST`
`MST_AFTER_DOING_SO.ANNOT`	`AFTER_DOING_SO.MST`