Q: Does the natural language processing ability support English or other foreign languages? A: Since most of the training corpus is Chinese, it has a good effect in the Chinese field at present, and English or other foreign languages are not supported temporarily.
Q: What is the input code? A: Currently, GBK code and UTF-8 code are supported.
Q: What are the meanings of POS tags in lexical analysis results? A: See the following table for details. Please refer to the API documentation for details.
| Part of speech |
meaning |
Part of speech |
meaning |
Part of speech |
meaning |
Part of speech |
meaning |
| n |
Common nouns |
f |
Locality noun |
s |
Locative noun |
t |
Time noun |
| nr |
name |
ns |
place name |
nt |
Organization name |
nw |
Title |
| nz |
Other proper names |
v |
Common verb |
vd |
verbal adverb |
vn |
Noun verb |
| a |
adjective |
ad |
coverb |
an |
Noun adjective |
d |
adverb |
| m |
Quantifier |
q |
classifier |
r |
pronoun |
p |
preposition |
| c |
conjunction |
u |
auxiliary word |
xc |
Other function words |
w |
punctuation |
Q: What are the dependency relation tags?
A: We can analyze 34 syntactic dependencies. Please refer to the API documentation for details.
- Centering relation ATT
- Quantity relation QUN (quantity)
- COO (coordinate)
- Appositive APP
- ADJ (adjunct)
- VOB (verb object)
- POB (position object)
- Subject verb relation SBV (subject verb)
- Similarity
- Time relation TMP (temporary)
- LOC (local)
- "De" word structure DE
- "Di" structure DI
- "De" word structure DEI
- "Suo" structure SUO
- "Ba" structure BA
- BEI structure
- Medium structure ADV (adaptive)
- Dynamic compensation structure CMP (completion)
- Concurrent structure DBL (double)
- Conjunction
- Associated structure
- Voice structure MT (mood sense)
- Verb verb structure VV (verb verb)
- Core HED (head)
- Pre object FOB (fronting object)
- Double object
- Top topics
- Independent structure
- Independent clause
- Dependent clause
- Reduplication relation VNV (verb no verb or verb one verb)
- One word YGC
- Punctuation WP
Q: What is the limit of short text similarity on the number of characters? A: The maximum length is 512 bytes, which is about 266 Chinese characters, but too many or too few words will have a slight impact on the effect.
Q: How to calculate the similarity of short text, and how to deal with mixed Chinese and English? A: The model vocabulary contains frequently used high-frequency English words, which can match the "Chinese English mixed arrangement" text in the Chinese context very well.
Q: Why does short text similarity calculation sometimes fail to return results? A: The prerequisite for returning results is that the words in the text are included in the vocabulary. Although the model vocabulary is large (millions of words), the problem of not being in the vocabulary still occasionally occurs. When all words in the text are not in the vocabulary, no results will be obtained.
Q: Is there a limit on the length of comments input for comment extraction? A: The maximum length is 10240 bytes, about 5120 Chinese characters.
Q: Can comment opinion extraction mark the text location of the mined opinion? A: Yes, the output result contains the position of the opinion label in the original text. For example, you can mark that the service of this hotel is good.
Q: Does comment opinion extraction support user-defined dictionary upload? A: The customized version of comment opinion extraction was officially opened in July 2018, which can support users to upload and expand the content of 13 vertical categories of opinion expression vocabulary to ensure the definition and extraction of richer comment content. See the official website document introduction and function introduction for details.
Q: Can comments be uploaded and summarized in batches? A: The interface can be used to realize this function. The interface can realize the tag extraction and polarity analysis of each comment, and multiple calls can realize the tag mining and analysis of multiple comments.
Q: What types of emotions can be analyzed by affective orientation analysis? A: The current analysis of emotional polarity is divided into positive, negative and neutral.
Q: What are the differences between affective orientation analysis and conversational emotion recognition? A: Conversation emotion recognition is an intuitive detection of the positive/neutral/negative language (such as: how awesome you are/how bored you are) in the user's conversation scene. Emotional analysis is more inclined to analyze the likes/dislikes expressed on an object (such as movies and books). The effect of the two is the best in the corresponding scene. Otherwise, the recognition accuracy will be affected to a certain extent.
For more questions, exchange here: https://ai.baidu.com/forum/topic/list/169
