Rosette Cloud and Enterprise Release Notes

• Bug fix: We fixed a bug in the Arabic statistical model and retrained the model.

• Latvian support added: Latvian lemmatization is supported. 

• Latin-script tokens in Russian: Latin-script regions within Russian inputs are now tokenized and analyzed as English. Example input: “мой новый iPhone”:

  • Previously: “iPhone” has part of speech FRGN.

  • Now: “iPhone” has part of speech PROP.

• Bug fix: When Rosette guessed German compounds they were sometimes lemmatized as verbs but tagged as nouns.

• Bug fix: There was a pairwise matching bug where Arabic name A vs Arabic name B produced a different result than Arabic name B vs Arabic name A.

• The fragment boundary detector now marks a boundary after any spaces following a fragment boundary delimiter.

• Bug fix: There was a sentence break after every Windows newline (i.e. carriage return + line feed).

• Bug fix: Multi-script Russian text would have a sentence break each time the script changed.

• Bug fix: There were unexpected sentence breaks after some short lines which did not end in whitespace.

• Bug fix: Sentence breaks were missing when the sentence break did not align with a token boundary.

• Latvian support added: Latvian tokenization is supported. 

• Configurable fragment boundaries: The delimiters for the fragment boundary detector are now configurable. A delimiter is restricted to a single character. To configure, edit the value of fragmentBoundaryDelimiters in the rbl-factory-config.yaml file. The string should contain all values that should be recognized as a fragment boundary, including any of the default values you want to keep.

• Bug fix: An underscore (U+005F) is no longer treated as a token separator in German when fstTokenize is enabled.

• Bug fix: Tokens from multi-script Russian text sometimes had incorrect offsets if fstTokenize was enabled.

• Improved address matching: We've boosted the score of tokens that differed only by an inserted or missing space.

  • Previously: The similarity score for "Old Colony Avenue" vs. "OldColony Avenue" was 0.44.

  • Now: The similarity score for "Old Colony Avenue" vs. "OldColony Avenue" is 1.0.

• Improved address matching: We've added support for additional Spanish and English address abbreviations, such as:

  • calle : cl

  • camino : cno, cmno

• Swedish: We have added entity extraction and linking support for Swedish (swe).

• Fragment boundary detection: The fragment boundary detector, which is used to better process data in lists and tables, is now on. Previously it was off. 

• New imperative Arabic verbs: We've added the imperative forms of 2000 Arabic verbs to the Arabic lexicon.

• Bug fix: In Russian, embedded spaces have been removed from the lemmas of numbers containing spaces. For example, the token "1 234" is now lemmatized to "1234" instead of "1 234".

• Bug fix: In Japanese, if a middle dot appeared in the input text immediately before a newline character, it was missing from the tokenized output. Now the middle dot gets a token in the output.

• Improved Japanese-English matching for organizations: We've expanded the Japanese-English token overrides for organizations. Overrides explicitly map nicknames, cognates, and variants to improve matching accuracy.

• Rosette Enterprise can now support multiple profiles for users that have different processing needs. Each profile consists of a specific set of parameter and configuration settings (e.g., case-insensitive on/off, entity linking on/off) and different data domains (e.g., enabling specific regex and gazetteer files). Custom profiles do not apply to the /address-similarity, /name-similarity, /name-deduplication, and /name-translation endpoints.

  Refer to the section Custom Profiles in the Enterprise User Guide for more details.

• Dynamic gazetteers: We’ve added a new endpoint, /entities/configuration/gazetteer/add, to add gazetteer entries to the /entities endpoint without having to stop and restart Rosette Enterprise.

  Refer to the section Adding Dynamic Gazetteers in the Enterprise User Guide for more details.

• Fragment boundary detection: The fragment boundary detector, which is used to better process data in lists and tables, is now on by default. Previously it was off by default.

  To turn off fragment boundary detection:

  • Edit the file: /config/rosapi/rbl-factory-config.yaml

  • Set fragmentBoundaryDetection: false

• New model for Katakana-Latin name matching: We've added a new deep learning model that improves Katakana-Latin name matching. It is enabled by setting enableSeq2SeqTokenScorer to true. Note that when true, all Japanese names, not just those in Katakana, will be scored with this new model, which may result in lower accuracy for Japanese names in Hiragana or Kanji. Improvements are planned.

• New parameter: We've added a new static parameter, katakanaTransliterationOnly, which defaults to false. Setting it to true will cause Japanese names written in Katakana to only be transliterated, not translated. This goes into the jpn_eng: section in the parameter_defs.yaml file.

  • ジョージ・ブッシュ translated to English as: George Bush

  • ジョージ・ブッシュ transliterated to English as: Jyo-ji Busshu

  Refer to the section Name Similarity Configuration Files in the Enterprise User Guide for more details.

• Fixed a bug where translating names from Arabic to English could cause a crash.

• New endpoint Address Similarity: We’ve added a new endpoint, /address-similarity, which performs a field by field comparison of two addresses and returns a similarity score between 0 and 1. Note that this endpoint is still in LABS and subject to change. Send us your feedback!

• Bug fix: Incorporates Chinese tokenization fix from /morphology.

• Bug fix: The Chinese tokenizer could create tokens at the end of the input string for temporal nouns without checking that the context was valid for temporal nouns.

  • Previously: 3分 (meaning “3 minutes” or “3 parts”) was detected as one token at the end of input but two tokens in most other contexts.

  • Now: 3分 is detected as two tokens at the end of input.

• Additional token overrides: We’ve added token overrides to the English-English override file and a new fullname override to the Japanese-English fullname override file.

• Bug fix: The Rosette install directory name can now contain spaces.

• Bug fix: When fstTokenizeis enabled, Russian words hyphenated with a number are now tagged with the part of speech of the word without the number.

  • Previously:Аполлона-11 was tagged PROP, MISC, and NOUN.

  • Now: Аполлона-11is tagged NOUN.

• Bug fix: We fixed a bug where transliterations from Arabic to English did not have all expected vowels.

  • Previously: تهران was translated to Thran

  • Now: تهران is translated to Tehran

• New Feature - PermID Linking (LABS): We have augmented QID (Wikidata) linked entities by also linking them to Thomson Reuters Permanent Identifiers (PermIDs) when PermIDs are included in the Wikidata entry. To access these identifiers, add {"options": {"includePermID": true}} to your call. For more information, see the Features and Functions. Note that this feature is still in LABS and subject to change. Please try it out and send us your feedback! 

• Wikidata update: We’ve refreshed and re-indexed the internal database for Wikidata linking. As a result, QIDs for some entities may have changed from previous versions.

• DBpedia update: A QID can now be associated with more than one DBpedia subtype. A new option parameter, includeDBpediaTypes, has been added. A new response field, dbpediaTypes, is a list that can contain one or more string values. The original response field, dbpediaType, will continue to return a single string: the first value on the list. The original option and response field are now deprecated and will be removed in a future release. If you are using DBpedia values, please migrate to the new fields.

• Bug fix: We fixed a bug in entity extraction and linking where it would crash when the modelType option is set to DNN, the linkEntities option is set to true, and the Entity Linker and DNN mentions overlap.

• Malaysian update: Language identification now returns zsm for Standard Malay (Malaysian) when using both long string and short string algorithms. Previously, zsm was returned  from the short string algorithm and msa (Malay macrolanguage) from the long string algorithm. This may require updates if you have code looking for the language code msa.

• Increased short string language coverage: Language identification's standard and short-string algorithms now have identical language coverage (except for transliterated Arabic-script languages). Added support for Albanian, Bulgarian, Catalan, Croatian, Estonian, Icelandic, Kurdish (Arabic script), Kurdish (Latin script), Latvian, Lithuanian, Macedonian, Polish, Serbian (Cyrillic script), Serbian (Latin script), Slovak, Slovenian, Somali, Tagalog, Ukrainian, Urdu (Arabic script), Uzbek (Cyrillic script), Uzbek (Latin script), and Vietnamese to the short string language identification algorithm.

• Multilingual short string support: For sufficiently short strings, language identification will now use its short-string algorithm in multilingual mode. 

• Polish update: Some Polish words ending in -cku, -ska, or -sku are now (following a different convention) lemmatized to forms ending in -cki or -ski. They had previously been lemmatized to their surface forms.

• Bug fix: The Japanese POS tag NE remained NE and was not converted correctly to UPT-16 (Universal POS tagset). NE is now correctly converted to the UPT-16 PART.

• Bug fix: The French POS tag CONJQUE was converted to UPT-16 CONJ. It is now converted to the more appropriate SCONJ.

• Bug fix: Chinese punctuation was tagged as GUESS when alternativeTokenization was disabled. Chinese punctuation is now tagged as PUNCT or EOS.

• Improved Chinese-Chinese name matching: We now give greater weight to names whose romanization is identical.

  • Previously: The similarity score for 李尚福 (li shangfu) and 李商复 (li shangfu) was 0.726.

  • Now: The similarity score for 李尚福 and 李商复 is 0.981.

• Improved name matching and translation of Japanese by normalizing small Katakana characters into their full-sized counterparts.

  • Previously: Small Ka in 田中ヵ wasn’t being transliterated and the following readings were generated instead (tanaka ヵ||denchuu ヵ||tana ヵ||tianzhong ヵ||jeonjung ヵ||tanaka ヵ).

  • Now: Small Ka in 田中ヵ now gets normalized to 田中カ and the following readings get generated (tanaka ka||denchuu ka||tana ka||tianzhong ka||jeonjung ka||tanaka ka).

• Improved name matching and translation in Chinese by normalizing Extension A characters.

  • Previously:㨗 in 㨗報 used to get ignored and the following readings were generated (bao||mitsugi||cubbon).

  • Now:㨗 in 㨗報 maps properly to its variant and now the following readings get generated (jie bao||shou mitsugi||cubbon).

• Token frequency used for document vectors all languages: For all languages, document vectors are now calculated using token frequency information. This information gives more weight to less common words, which are more significant to the overall meaning of the document. Previously, only English vectors were weighted this way.

• Entity linking is disabled (linkEntities: false) 

• No supplemental regular expressions are loaded (supplementalRegularExpressionPaths null)

• Pronominal resolution is not enabled (resolvePronouns: false)

• caseSensitivity: caseSensitive

• Japanese Update:  Japanese data regex now includes the new era 令和 (Reiwa), which started May 1, 2019.

• Bug fix: We've fixed additional cases where Japanese characters were wrongly normalized into their simplified Chinese equivalents in entity linking. This issue was also addressed in the 1.13.0 release.

• Bug fix: We've added missing parameter files for linking models, which may improve accuracy.

• Updated lexicons: We've made various small updates to the lexicons of German, English, and Swedish. 

• Improved Arabic analyzer: The Arabic analyzer will attempt to replace leading hamzated alefs with plain alefs for unrecognized tokens, to see if the version with the plain alef is recognized.

• Improved Hebrew normalization: U+2019 RIGHT SINGLE QUOTATION MARK and U+201D RIGHT DOUBLE QUOTATION MARK are now normalized to U+0027 APOSTROPHE and U+0022 QUOTATION MARK in Hebrew, to support their use as geresh and gershayim.

• Bug fix: We've fixed a bug which occurred when Rosette encountered a Hebrew token consisting of multiple prefixes without a base. In this case, Rosette returned an incomplete surface form, i.e., only containing the first prefix. For example, the surface form of the token “מה” should be  “מה” but was being returned as “מ”. With the bug fix the surface form returned now is “מה”.

• Bug fix: We’ve fixed a problem where closing parentheses, brackets, and braces that follow URLs were merged into the URLs.

• Bug fix: The disambiguator is now more likely to select analyses for @mentions, email addresses, hashtags, and URLs over other analyses.

• Bug fix: When the Hebrew tokenizer encounters a character not used in Hebrew immediately following a character used in Hebrew, it now starts a new token. Formerly, it would delete that character and any following characters up to the next token separator (e.g. whitespace).

• Bug fix: Hebrew tokens consisting of multiple prefixes without a base are now tagged with the part of speech “unknown”, to match single-prefix tokens. Previously, multi-prefix tokens were not tagged with any part of speech.

• Bug fix: When fstTokenize is enabled, we’ve fixed a problem where Russian hyphenated words that end in numbers, like “Аполлона-11”, were not tagged as DIG. They are now tagged with the same parts of speech they had before Rosette API 1.12.2

• rex-root-<version>.tar.gz

• rex-root-<version>-eng.tar.gz for English language files

• rex-root-<version>-deu.tar.gz for German language files

• tvec-root-<version>-eng.tar.gz for English language files

• tvec-root-<version>-deu.tar.gz for German language files

•  Expanded English support: We can now process English with a case-insensitive model by providing (uen) as the input language.

• Bug fix: We’ve fixed a problem in Japanese where entity names that include the middle dot were not being handled correctly. Entity names that include a middle dot are no longer split into two entities.

• Bug fix: We’ve fixed a problem in Japanese entity linking where, in some cases, Japanese characters were being replaced with Chinese characters.

• Bug fix: We’ve fixed a problem where, in some cases, entities were mislabeled when the includeDbPediaTypes option was not flagged.

• New Hebrew disambiguator: We’ve added a new default disambiguator (perceptron) for Hebrew. Use the disambiguatorType option to enable another disambiguator (DNN or dictionary). For example, to return to the previous default, add {"options": {"disambiguatorType": "DNN"}} to your call.

• Bug fix: We’ve fixed an error where some Chinese tokens included spurious white space characters.

• Bug fix: We’ve fixed a problem where extremely long tokens (thousands of characters) would slow down the tokenizer.

• Bug fix: We’ve fixed a  problem where Polish tokens that can appear in multiword expressions were lemmatized to full expressions, even when the full expression wasn’t in the input.

  • Previously:dzień lemmatized to dzień_dobry.

  • Now:dzień is lemmatized to dzień.

• Bug fix: We've fixed a problem where the non-final components of Russian compound words with more than one hyphen were not lemmatized correctly.

• Hungarian: We’ve added a Hungarian frequency language model for LOCATION entity type and retrained the language model for PERSON entity type, improving Hungarian name matching.

  PERSON:

  • Previously: The similarity score for Domonkos Gyula Tiborné and Domonkos Gy. Lászlóné was 0.697.

  • Now: The similarity score for Domonkos Gyula Tiborné and Domonkos Gy. Lászlóné is 0.775.

  LOCATION:

  • Previously: The similarity score for Szentmihály puszta and Szentmihály pihenő was 0.576.

  • Now: The similarity score for Szentmihály puszta and Szentmihály pihenő is 0.696.

•  Expanded English support: We can now process English with a case-insensitive model by providing (uen) as the input language. Be aware that when using DNN for the modelType the accuracy of the results may be lower than when analyzing standard, sentence-cased, English input.

• Bug fix: We’ve fixed a bug where, under some circumstances, the head mention for an extracted entity was not correctly identified.

• Bug fix: We've fixed a bug where the Persian lemmatizer did not add lemmas to the first analyses of many tokens, especially verbs. 

• Bug fix: We've fixed a bug where after some sequences of 4096 characters, containing mostly white space and at most one token, any following tokens had incorrect original offsets.

• Bug fix: We've fixed a bug where capitalized tokens could return an out-of-vocabulary token-level embedding instead of the embedding consistent with their lowercase form.

• Bug fix: Previously, the Semantic Vectors endpoint did not always return vectors of a consistent length. Now, returned vectors will always be normalized to have a length of one.

• New endpoint Similar Terms:  We've added a new endpoint, /semantics/similar, which uses text vectors to generate multilingual related terms with numerical similarity scores for any input word(s) in Arabic, English, Chinese, German, Japanese, North or South Korean, Russian, or Spanish. For more information, see the Features and Functions.

  • Input Term: spy returns

  • Spanish
    {"term":"espía","similarity":0.61295485},
    {"term":"cia","similarity":0.46201307},
    {"term":"desertor","similarity":0.42849663},
    {"term":"cómplice","similarity":0.36646274},
    {"term":"subrepticiamente","similarity":0.36629659}
    German
    {"term":"Deckname","similarity":0.51391315},
    {"term":"GRU","similarity":0.50809389},
    {"term":"Spion","similarity":0.50051737},
    {"term":"KGB","similarity":0.49981388},
    {"term":"Informant","similarity":0.48774603},
    Japanese
    {"term":"スパイ","similarity":0.5544399},
    {"term":"諜報","similarity":0.46903181},
    {"term":"MI6","similarity":0.46344957},
    {"term":"殺し屋","similarity":0.41098994},
    {"term":"正体","similarity":0.40109193},
    

• Semantic Vectors: The /text-embedding endpoint has been renamed to /semantics/vector. While the /text-embedding endpoint will remain accessible through April, we encourage you to migrate as soon as possible to avoid missing any important updates.

• Improved linking confidence: We've updated the linking confidence calculation and thresholds to improve accuracy.

• Supported languages: We've removed xxx from the list of languages returned when using /entities/supported-languages.

• Bug fix: We’ve fixed a bug where the salience score was not always returned for entities with pronominal mentions, when requested.

• Bug fix: We've fixed a bug where some later entity mentions that were chained to the first mention of a given entity were not always properly returned.

• Bug fix: We've fixed a bug where sometimes a null pointer exception was returned when resolving pronouns.

• Bug fix: We’ve fixed a bug where some English words were automatically getting tagged as proper nouns when capitalized. 

• Bug fix: We’ve fixed a bug in English where “people” was not being properly lemmatized to “person”. It now has the lemma candidate “person” when appropriate.

• Bug fix: We’ve fixed a bug where ordinal numbers and comparative adjectives in English like “second” and “lower” were analyzed as verbs.

• Hungarian: We’ve added support for multi-letter initials in Hungarian, improving Hungarian name matching.

  • Previously: The similarity score for Kovács Cs. István and Kovács Csaba István was 0.68.

  • Now: The similarity score for Kovács Cs. István and Kovács Csaba István is 0.91.

• Japanese: We’ve improved the accuracy of matching between katakana and kanji versions of Japanese organization names.

  • Previously: The similarity score for ドクリツギョウセイホウジンニホンガクジュツシンコウカイ and 独立行政法人日本学術振興会 was 0.41.

  • Now: The similarity score for ドクリツギョウセイホウジンニホンガクジュツシンコウカイ and 独立行政法人日本学術振興会 score is 0.72.

• Chinese: We’ve improved the accuracy of matching Chinese organization names.

  • Previously: The similarity score for 松下能源(上海)有限公司  and Panasonic Energy (Shanghai) Co., Ltd  was 0.58.

  • Now: The similarity score for 松下能源(上海)有限公司  and Panasonic Energy (Shanghai) Co., Ltd  is 0.82.

• We've decreased warm-up time by only loading licensed languages when Rosette is set to pre-warm.

• We’ve added the ability for on-premise users to have more control over configuration options when using custom-trained models.

• We've reduced the minimum memory requirements to 16GB of RAM for the entity extraction and linking, sentiment analysis, and topic extraction endpoints.

• We’ve improved the efficiency of the initial load time for the entity extraction and linking endpoint.

• All client bindings have been updated to support the /semantics/vector and /semantics/similar endpoints.

• The examples have been modified in the Python bindings to demonstrate how to set options.

• Korean: We've improved the accuracy of Korean extraction, largely through better handling of Josa (postpositions) and compound words.

• Entity Linking: We've added support for entity linking to Wikipedia for both the top level types (PERSON, LOCATION, ORGANIZATION, ETC.) as well as the over 700 DBpedia types (see full list here) in the remaining 16 languages supported by entity extraction. This is in addition to the languages currently supported by entity linking: Chinese, English, Japanese, and Spanish.

• Hebrew disambiguation: We’ve improved analysis in Hebrew by adding disambiguation, a mechanism for more accurately choosing which of several candidate analyses is provided in the response. For Hebrew only, we've added an option disambiguatorType to select which disambiguator is used. The values are DNN for the TensorFlow-based deep neural network model and dictionary for the dictionary-based model. The default is dictionary. To enable the DNN disambiguator, add {"options": {"disambiguator": "DNN"}} to your call.  

• Persian lemmatization: We’ve added lemmatization support to Persian.

• Chinese organization names: We've improved the accuracy of matching between Chinese and English organization names.

  Previously: The similarity score for 索尔维－恒昌（张家港）精细化工有限公司 and Solvay-Hengchang (Zhangjiagang) Fine Chemicals Co., Ltd was 0.6929.

  Now: The similarity score for 索尔维－恒昌（张家港）精细化工有限公司 and Solvay-Hengchang (Zhangjiagang) Fine Chemicals Co., Ltd score is 0.8225.

• The minimum system requirements for running Rosette Enterprise have changed for some use cases. This is a result of providing entity linking for 16 additional languages in this release. We now support entity linking for all 20 languages supported by entity extraction.

  The entity extraction and linking, sentiment analysis, and topic extraction endpoints require significant memory allocation. If using these endpoints, the new minimum memory requirements are:

  • 32GB RAM

  • 64GB of disk space (more may be needed for growing logs)

  For all other endpoints, the minimum memory requirements are:

  • 16GB RAM

  • 35GB of disk space (more may be needed for growing logs)

• Rosette Enterprise is now available as a Docker container. The images are available on Docker Hub. A Basis shipment, containing a license file and a docker-compose file customized to your licensed endpoints, is still required.

Morphological Analysis /morphology/{morphoFeature} 

• Chinese: We’ve added the word “百度” to the Chinese lexicon. This only has an effect when modelType is set to default, which is its default value.

  Previous:  Two tokens: “百”, “度”

  Now: One token: “百度”

• German: The part of speech of the acronyms “MAN” and “MIT” is now NOUN in German, instead of falling back to the parts of speech of the unrelated words “man” and “mit”.

  Previous: MAN/INDPRO, MIT/PREP, MIT/VPREF, MIT/ADV

  Now: MAN/NOUN, MIT/NOUN

• Spanish: We’ve improved Spanish part-of-speech tag and lemma disambiguation.

  Previous: 91.646% POS accuracy and 90.243% lemma accuracy using the IULA Spanish LSP Treebank

  Now: 91.742% POS accuracy and 90.344% lemma accuracy

• New Model: We’ve added a new model for increased accuracy when matching Hungarian names to other Hungarian names. 

  Previous: Pavlovitch Bryulov vs. Pavlovics Brjullov - score: 0.84

  Now: Pavlovitch Bryulov vs. Pavlovics Brjullov - score: 0.95

• Bug Fix: Fixed a bug involving duplicate readings produced when transliterating Chinese names.

  Previous: Wang Xing vs. 王行 - score: 0.69

  Now: Wang Xing vs. 王行 - score: 0.99

• Improved Chinese accuracy: We've replaced the underlying tokenizer to improve accuracy in Chinese.

• Improved Hungarian accuracy: We've updated our pattern match extractors in Hungarian. This improves accuracy for the MONEY and DATE types.

• Known issue: We've fixed a bug where entity mentions were being miscounted if the calculateSalience option was set to true.

• Improved Dutch disambiguation: We've improved Dutch part-of-speech disambiguation.

• Improved Japanese and Chinese lemma support: In the last release, most Japanese and Chinese tokens did not have lemmas when modelType was set to default. Now, such tokens have lemmas equivalent to their surface forms.

• New per-endpoint licensing: Endpoints are now activated directly from your installed license. The endpoints.yaml file has been removed from the installation.

• New Enterprise User guide: We’ve added a user guide (rosette-enterprise-user-guide-1.11.0.pdf), that provides new content and  replaces the files rosette-api-on-premise-install-guide-1.11.0.txt, overview.md, and Rosette_API_Embedded_User_Guide.pdf.

• Simplified installation for macOS and Linux: The installation for both RESTful and embedded Java has been simplified for macOS and Linux. Installation for Windows has not changed, but detailed installation notes are now included as part of the new Enterprise User Guide.

• Name Changes: We are continuing to consolidate and simplify our branding. Rosette API On-Premise is now Rosette Enterprise. We’ve made changes to the documentation and license names to reflect.

• New supported languages sub-endpoints: For all endpoints (excluding Name Similarity, Name Translation, and Name Deduplication), Rosette now provides a GET /rest/v1/<endpoint>/supported-languages method that returns that endpoint's supported languages and scripts. See the Features and Functions or the Interactive Docs for more information.

• Updated bindings: We've updated our CSharp and Java bindings. Be sure to get the latest version (1.11.0) to take advantage of all the new features and improvements!

• New language: Rosette now supports name deduplication of Hungarian names.

• Multilabel categorization: Rosette can now return multiple category labels per document.  For more information, see the Features and Functions. To return only a single category label per document, set the {"options": {"singleLabel": true}}. For more information, see the Features and Functions.

• New languages: The text embedding endpoint now supports Russian, North Korean, South Korean, and Arabic.

• Individual token embeddings: We can now return embeddings for individual input tokens. To enable per-token embeddings, add {"options": {"perToken": true}} to your call.

• Response modifications: We've made changes to the text embeddings endpoint's response structure. Document-level embeddings now have their own  dedicated slot embeddings and will no longer appear in documentMetadata. Please note that this is a breaking change, contact Rosette support for more information.

• New feature - DBpedia Types (LABS): We've added over 700 new entity types to the Entity Extraction and Linking endpoint, drawn from the DBpedia ontology. To access these entity types, add {"options": {"includeDBpediaType" = true}} to your call. You'll notice more than 10 additional macro types in the type field as well as the all new DBpediaType field. For more information, see the Features and Functions. Note that this feature is still in LABS and subject to change. Send us your thoughts!

• Better accuracy: We've improved the recall of Rosette's entity linking across all supported languages.

• New language: Entity extraction now supports Hungarian.

• Known issue: MONEY, PHONE NUMBER, and URL types are not extracting properly in Hungarian. This will be fixed in the September 2018 patch release.

• Support for North and South Korean added: Rosette can now identify North Korean (qkp) and South Korean (qkr) dialects. To enable the dialects, add { "options": { "koreanDialects":true }} to your call.

• New algorithm for Chinese and Japanese: We've added a new algorithm for Chinese and Japanese morphological analysis. Prior to version 1.11.0, the default algorithm was perceptron. To return to the old model, add {"options": {"modelType": "perceptron"}} to the body of your call.

• Norwegian lemmatization: We’ve expanded the lemma dictionaries for Norwegian, both Bokmål and Nynorsk.

• Improved English and Spanish disambiguation We've improved the accuracy of lemmatization and part of speech tagging in both English and Spanish.

• Bug fix: We've improved handling of formatting characters in German. 

• Bug fix: We’ve fixed a bug where the Hebrew POS tag wPrefix was not converted to UPT-16.

• New algorithm for Chinese and Japanese: We've added a new algorithm for Chinese and Japanese tokenization. Prior to version 1.11.0, the default algorithm was perceptron. To return to the old model, add {"options": {"modelType": "perceptron"}} to the body of your call.

• Bug fix: We’ve fixed a bug where in Catalan, in which Rosette did not tokenize after an apostrophe in cases where the apostrophe marks a token boundary.

• Bug fix: We've fixed a big in Japanese, in which Rosette did not recognize 々 as a Japanese character, so it was considered its own token.

• New language: Name similarity now supports matching between Hungarian and English names.

• Improved language-of-origin detection: We've improved the detection of language-of-origin of Japanese names written in Katakana.

• Improved Arabic script segmentation: We've improved segmentation of Persian (Dari and Farsi), Pushto, and Urdu names written in Arabic script.

• New Japanese tokenizer: We've replaced the underlying tokenizer to improve accuracy for Japanese.

• Bug fix: We've fixed a bug where some components of compound German words were incorrect when the surface form of the component could be either a noun or a verb. 

• Bug fix: We've fixed a bug where the Hebrew parts of speech tags did not use UPT-16, the POS tag set used by the other languages.

• Bug fix: We've fixed a bug where returned email addresses and URLs could contain control or whitespace characters.

• Bug fix: We've fixed a bug where returned Hebrew tokens could contain control characters or nothing but default ignorable characters.

• Bug fix: We've fixed a bug where Chinese, Japanese, and Thai tokens could contain control characters.

• Bug fix: We've fixed a bug where the default Japanese tokenizer truncated some katakana tokens when they appeared after non-katakana tokens.

• Name Deduplication input limit: Each call now has a limit of 1000 names per list.

• Improved Entity Level Sentiment Analysis: We've improved our calculation of entity level sentiment to more accurately consider the context around each mention. Please note, this update may cause results to change.

• Bug fix: We've fixed a bug where the initial token dependency for every sentence (other than the first) was omitted from the list of results.

• Improved Hebrew Entity Extraction: We've improved Hebrew entity extraction by removing superfluous prefixes from extracted entities.

• Improved Confidence Scores: We've improved statistical model confidence scores to provide a more effective tradeoff between precision and recall. Please note, this update may cause results to change. If you have set a threshold based on entity confidence scores, please evaluate to ensure optimal performance.

• Improved Entity Normalization: Social media characters such as "@" and "#" are removed from a Mentions normalized string. Offsets to the original string data field remain the same.

• Bug fix: Previously, the Dutch disambiguator would always choose analyses whose lemmas matched their surface forms, even for very rare lemmas; now the more common lemma will be returned. For example, schepen can be a singular noun with the lemma schepen, but it is more likely to be a plural noun with the lemma schip.

• New deep neural network processor (in BETA): We've added an alternative entity extraction processor, which can be used in place of the standard statistical extractor. The new processor employs a deep neural network that improves accuracy up to 7% and error rate up to 32%. It is available for English, Arabic, and Korean. To enable this processor, provide DNN for the modelType. Example: {"content": "your_text_here", "options": {"modelType": "DNN"}}

• New language support: We've added support for lemmatizing Catalan, Estonian, Serbian, and Slovak text.

• Bug fix: Previously, tokens could be empty or contain only invisible characters. Such tokens will no longer be returned.

• New language support: Short string language identification now also supports Malay and Indonesian. Both languages were already supported for longer texts.

• New language support: We've added support for sentiment analysis in Persian (Farsi and Dari) at both the document and the entity level.

• New language support: Rosette now supports transliteration of person, organization, and location names from Greek to Latin script.

• New language support: Rosette now supports matching of Greek names written in Greek script to English names written in Latin script and other Greek names written in Greek script.

• Accuracy improvements: We have improved match scores and segmentation rules for Arabic, Western Farsi, and Japanese names.

• Bug fix: We’ve improved our handling of tokens consisting of numbers and Latin characters, such as serial numbers, in Korean. Previously these tokens were decompounded into multiple morphemes.

• Bug fix: We’ve added the lower case Russian word “интернет” (“internet”) to the dictionary, which was previously only present in title case.

• Bug fix: We’ve improved our handling of tokens containing an apostrophe immediately followed by a digit in languages like French and Italian, like “all'M5S”. Previously, the apostrophe would be parsed as its own token.

• Bug fix: We’ve improved our German analysis by taking better advantage of context clues, and now return more accurate results, especially for uncommon words.

• Bug fix: We’ve improved our handling of English and German words in all-caps. Previously, these words were assumed to be proper nouns, even though all-caps may simply denote emphasis.

• Performance improvement: We’ve added caching of model objects to prevent OutOfMemoryErrors.

• Hebrew entity extraction: We’ve added support for entity type "Title" in Hebrew.

• German disambiguation: We’ve improved our German disambiguator for lemmas and part-of-speech tags to be more sensitive to capitalization, particularly for single word inputs.

• Bug fix: Previously, German definite articles (der, die, das, den, dem, and des) meaning the were lemmatized inconsistently. They are now all lemmatized to the masculine singular nominative form, der.

• Bug fix: In some languages, an apostrophe may mark a token boundary, like in the Italian phrase all'M5S. Previously the token boundary was incorrectly omitted when the following token contained a digit. This issue has been rectified and M5S will be properly tokenized.

• Farsi Name Matching: We’ve improved the behavior of Western Farsi-English matching by tokenizing input names earlier in the analysis process.

• Currency support: We’ve added several additional currency symbols to the regex, including the Turkish Lira (₺), the Pound Sterling (₤), and the Euro (€).

• Bug fix: We’ve fixed a bug that caused hexadecimal number strings to be incorrectly extracted as products.

• Thai improvement: We’ve modified the gemination rules (consonant elongation) of the ISO11940-2 Thai transliteration standard to improve Thai translation accuracy.

• Bug fix: We’ve fixed a bug around matching names of organizations and locations that contain numbers, such as "Century 21 Real Estate LLC." These non-person names containing digits will now match more accurately.

• Salience scores: We've added salience scores for keyphrases and concepts to indicate how relevant an extracted concept or keyphrase is to the overall content of a text. You now have the option to filter out results below a desired threshold value: {"content": "your_text_here", "options": {"keyphraseSalienceThreshold": value, "conceptSalienceThreshold": other_value}}. 

• Short string support: We've improved our concept extraction logic, and now support concept extraction for short input strings, i.e. texts less than 280 characters long.

• Thai support: Rosette now supports deduplication of Thai names.

• New feature: We've added the option to use an experimental alternative deep neural network (DNN) sentiment model for English: {"content": "your_text_here", "options": {"modelType": "DNN"}}. The new model will produce different results, which may be more accurate than the current support vector machine (SVM) model, depending on your data. As it is experimental, we are particularly interested in getting user feedback. On-premise users of Rosette API should review the new system requirements in install-guide.txt before using this option.

• Entity offsets returned: Entity mention offsets are now returned by default. Offsets can be used to locate the exact surface forms of an extracted entity in the document text.

• Korean improvements: We’ve significantly improved the accuracy of entity extraction results across all entity types in Korean.

• Confidence scores: Confidence scores for entities extracted using Rosette’s statistical processor, as well as all linked entities, will now be returned by default. Confidence scores allow Rosette to return the most accurate results, particularly for entity linking. To change this behavior, set {"content": "your_text_here", "options": {"calculateConfidence": false}}.

• Detect language regions in multilingual documents: The language identification endpoint can now detect different language regions in a multilingual document.

• Score changes: We’ve rescaled the confidence scores returned by the language identification endpoint based on customer feedback. The ranking of language candidates will not change, but the scores themselves will be higher. If you currently filter language identification results based on a confidence threshold, you will need to reset that threshold to maintain parity with previous versions.

• Thai support: Rosette now supports transliteration of names from Thai to Latin script.

• Thai support: Rosette now supports matching of Thai names to English names and other Thai names.

• Accuracy improvements: We have improved match scores for Arabic names (persons, locations and organizations) as well as for Chinese and Japanese organizations.

• Bug Fix: This release addresses a bug whereby entity linking confidence scores were not being returned when requested. Confidence scores for entities resolved to Wikipedia entries will now be returned when using the following option: {"content": "your_text_here", "options": {"calculateConfidence": true}}

• New Endpoint: Topic extraction We've added a topic extraction endpoint that identifies the key ideas of an input text. For a given input, the endpoint will return two lists: Keyphrases, a list of phrases extracted directly from the text, and Concepts, a list of phrases which do not have to be explicitly mentioned in the input.

• LABS graduates: The /transliteration, /relationships, and /syntax/dependencies endpoints have graduated from “Labs” status and are now fully supported.

• New language: Rosette now supports document and entity-level sentiment analysis in French.

• Salience Scoring: Rosette can now return salience scores, which indicate whether an entity is important to the overall scope of the document. Turn on the scores by adding an option to the request: {"content": "your_text_here", "options": {"calculateSalience": true}}

• Linking Confidence Scoring: Rosette can also now return Linking Confidence scores, which represent the degree of certainty of the link between an in-document entity mention and its linked QID. It may be used for thresholding and removal of false positives. Linking Confidence scores for entities identified by our linker and assigned with a QID are now available by adding an option to the request: {"content": "your_text_here", "options": {"calculateConfidence": true}}

• Bug fix: This release addresses a backward compatibility issue between the latest Rosette API and older versions of our Java binding that affected Rosette's ability to return entity confidence scores. Confidence scores for entities identified by our statistical extractor are now available by adding an option to the request: {"content": "your_text_here", "options": {"calculateConfidence": true}}

• New Endpoint: Transliteration We've added a transliteration endpoint that converts between Arabic written in ASCII, also called Romanized Arabic chat or Arabizi, and native Arabic script.

• Beta Arabic Support for /sentiment: We now return document-level and entity-level sentiment analysis results for Arabic language input.

• Personal pronoun resolution for /relationships: Building on the pronoun resolution capabilities of our /entities endpoint, pronouns which are resolved to named entities can now be arguments in relationships.

• Improved Confidence Scoring: Confidence score calculation is improved to correlate well with precision and may be used for thresholding and removal of false positives.

• New support for emoticons, emoji, @mentions, hashtags, URLs, and email addresses: These special characters and character combinations are now kept together as a single token in all languages, greatly improving the accuracy of analysis further downstream.

• Improved accuracy for English and Spanish: For this release, we updated our English and Spanish dictionaries. We also introduced new, more advanced disambiguation models for these languages, which help Rosette to correctly determine a given word’s part of speech. For example, words like “object” can be either a noun (“this is an object”) or a verb (“I object!”).

• Lemmatization and normalization of emoticons, emoji, @mentions, hashtags, URLs, and email addresses: Rosette now normalizes and lemmatizes these special characters and character combinations to streamline analysis.

• Improved decompounding for Dutch: Dutch language text is now decompounded more accurately, Dutch text is now decompounded more accurately, producing better tokens for search enhancement and other applications.

• Improved Accuracy of Corporate Relationships: Improvements made to the identification of relationships between corporations. The relationships involved are: ORG-SUBSIDIARY-OF, ORG-COLLABORATORS, ORG-ACQUIRED-BY and ORG-PROVIDER-TO.

• Removed the ORG-PARTNERSHIPS Relationship: The ORG-PARTNERSHIPS relationship is now subsumed under ORG-COLLABORATORS and is no longer extracted as an independent relationship.

• Improved Linking Accuracy via Inclusion of New Context Features: The statistical model for entity linking includes features that measure the vector space similarity between an entity context and the Wikipedia contexts of its potential linking targets. The new features result in higher F-Scores across all supported languages.

• Entity Linking in Japanese, Chinese and Spanish: Entity linking to Wikidata with QIDs for Japanese, Chinese and Spanish text is supported.

• Removed Long Text Linking: Entity linking to Wikidata (with QIDs) for long texts is removed, which, as a result, removed entity linking capabilities in Arabic.

• Vector dimension reduced from 512 to 300: We are able to produce smaller vectors that are more efficient and memory friendly without sacrificing overall speed or accuracy.

• Improved Speed and Accuracy: A number of speed enhancements have been made along with much larger vocabularies to increase accuracy.

• Improved Accuracy on Texts with Mixed Scripts: A script specific model is now selected based on the weighted frequency of the different scripts in the input.

• Japanese Improvements: Rosette API now has better support for Japanese name matching. This includes the new use of word embeddings, which are used to match words with similar semantic meaning, as well as improved Japanese name segmentation.

• New Endpoint Functionality: The /relationships endpoint now returns targeted relationships, as opposed to the former open relationships, as its default extracted relationships. Targeted relationships are specifically between two entities, and are labeled by a certain relationship type. You can see the former open relationships by setting the option of "discoveryMode" to "true".

• Removed Deprecated Endpoint: The /entities/linked endpoint, previously deprecated, is now completely removed. All functionality is available through the /entities endpoint. You will receive a 404 when calling /entities/linked.

• Social Media Linking in Japanese and Chinese: Our fast short text entity linker to Wikidata is now available for Japanese and Chinese.

• Removal of long text entity linking: Our long text entity linker has been replaced by our fast short string entity linker. You will now see entity linking results from our short string linker by default. This removes linking support for Arabic.

• Additional Language Support: The entity extractor now supports Vietnamese.

• Name Translation and Similarity CJK Improvements: The /name-similarity and /name-translation endpoints now support matching and translating between Japanese-Chinese, Japanese-Korean, and Korean-Chinese. Japanese accuracy was improved significantly.

• Improved Accuracy for Document-level Embeddings: We made some improvements to our algorithm for calculating text embeddings across multi-word input, so you should see more accurate results for document-level vectors.

• Beta Japanese Support for /sentiment: We now return document-level and entity-level sentiment analysis results for Japanese language input.

• New Endpoint: We've added a syntactic dependencies endpoint that returns the parse tree of the input text as a list of labeled directed links between tokens, as well as the list of tokens in the input sentence.

• Entities Linked to Wikidata: Where available, Rosette will now link entities extracted within relationships to Wikidata. You'll see this information returned as a QID in the argument ID.

• Modality Returned: We've also added a "modality" field to Rosette's Relationship Extraction. Modality is the semantic context of the possibility or necessity of the relationship; the values can be “assertion”, “negation”, “uncertainty”, “opinion”, or “question”.

• New $99 API Plan: For a limited time, we’re offering a special Starter plan. $99/month gets you 40,000 Rosette API calls. Want to dive deep into Rosette but don’t need a whole 100,000 calls? This plan is for you.

• New Endpoint: We added a text embedding endpoint that returns a single vector of floating point numbers that represents the document or word in the semantic space.

• Additional entities: We changed the /sentiment endpoint to return the sentiment of all entities discovered by Rosette, including Person, Location, Organization, Date, Time, and more entity types.

• Turn off entity linking: We added an option to disable entity linking in order to improve the call speed. Add "options": {"linkEntities": "false"} to your /entities call. Rosette returns a list of the entities with a temporary ID (TID).

• Concurrency header: We added the X-RosetteAPI-Concurrency header to return the number of concurrent calls allowed on your plan. If you are receiving 429 errors, Too Many Requests, then Contact us for greater concurrency.

• Input genre: The genre field is available for /entities and /entities/linked to indicate the input is from social media. Specifying genre=social-media does not affect the output of the other endpoints. Applies to: /entities, /entities/linked, /relationships, /categories, /sentiment, /language, /morphology, /tokens, /sentences.

• Temporary entity ID: With the unification of the /entities/linked and /entities endpoints, the /entities/linked now returns a “T” ID for entities without a Wikidata QID.

• Entity endpoints unified: We combined the /entities and /entities/linked endpoints into one endpoint, /entities. Rosette now returns the entity mentions and the entityId, if available. The entityId replaced the indocChainId. The output of /sentiment has not changed.

• Entities Linked deprecated: We deprecated the /entities/linked endpoint. It is still available, but we recommend that you adapt your applications to the new /entities endpoint.

• Additional entities: Rosette now extracts more entity types: Date, Time, Longitude and Latitude, and Distance.

• Japanese entityId: We added support for linking entities in Japanese (jpn) text to their entityId.

• Spanish social media: We added support for extracting entities from social-media in Spanish language documents, using the genre field.

• Malay entities: We added support for extracting entities in Malay (msa).

• 409 Error: We added the 409 error code for when the binding version is out of date. If you receive this error, update your binding to the most recent version.

• Spanish support: We added support for analyzing the sentiment of Spanish language documents.

• Ruby: We added the Ruby binding to the gray column to the right and on Github. There is a Ruby gem available as well.

• R: We added the R binding to the gray column to the right and on Github.

• cURL examples: We changed the shell examples in the gray column on Features and Functions to be cURL code examples.

• Social input: We added a request field, "genre": "social-media", to speed up and improve the accuracy of linking Person, Location, Organization and Product entities in social media posts. English input only.

• Language used: We added a Response Header, X-RosetteAPI-ProcessedLanguage, to return the language used by Rosette for processing the call. Applies to: /entities, /entities/linked, /relationships, /categories, /sentiment, /language, /morphology, /tokens, /sentences

• requestId moved: We moved the requestId object from the JSON response body to the Response Header as X-RosetteAPI-Request-Id. Applies to: /entities, /entities/linked, /relationships, /categories, /sentiment, /language, /morphology, /tokens, /sentences, /name-translation, /name-similarity

• Rosette API Key: We changed the user_key header’s name to X-RosetteAPI-Key. The user_key header is deprecated. Applies to: /entities, /entities/linked, /relationships, /categories, /sentiment, /language, /morphology, /tokens, /sentences, /name-translation, /name-similarity

• unit parameter removed: We removed the optional unit request parameter. All input will be handled as a doc. Applies to: /entities, /entities/linked, /relationships, /sentiment, /morphology

• Base64: We removed support for Base64 encoding. You can submit binary files as a multipart/form-data call type. Applies to: /entities, /entities/linked, /relationships, /categories, /sentiment, /language, /morphology, /tokens, /sentences

• Confidence removed: The confidence value has been removed from the response object.

• Accuracy mode: We removed the optional accuracy mode. All input will be processed with the precision accuracy mode, so Rosette will return a precise list of accurate relationships.

• Explanations removed: The explanations value has been removed from the response object.

• Explanations removed: The explanations value has been removed from the response object.

• Entity sentiment: We added support for entity-level sentiment analysis. The JSON response for the /sentiment endpoint now includes two objects – document and entities. See the interactive documentation for examples of this new response.

• Neutral result: We added a neutral label for documents and entities with a neutral sentiment.

• Short strings: Rosette will automatically process short and long content with our proprietary algorithm for sentiment analysis.

• Explanations removed: The explanations value has been removed from the response object.

• Added language support: We added language support for Dari, Persian, Urdu, and Western Farsi for Parts-of-Speech Tags.

• Universal POS Tags: We return Universal Parts-of-Speech Tags for all supported languages.

• Tokens list: Rosette returns parallel lists of tokens, lemmas, compound components, parts-of-speech tags, and Han-readings. If a token does not have a lemma, compound component, POS tag, or Han-reading, or if the language is not supported, then Rosette will return “null” in that list.

• Renamed to /name-translation: To clarify the endpoint’s function, we renamed /translated-name to /name-translation. /translated-name is no longer available.

• Removed result layer: Within the response to /name-translation and /name-similarity endpoints, we removed the result layer so the results are in the response object. Also applies to: /name-similarity

• TargetScheme requires uppercase: For advanced users who would like to specify a targetScheme, the scheme must be submitted in uppercase.

• Renamed to /name-similarity: To clarify the endpoint’s function, we renamed /matched-name to /name-similarity. /matched-name is no longer available.