1. Lexical: Words vs. Characters Syntactic and Stylistic
Predicting Foreign Language Usage from English-Only Social Media Posts
Svitlana Volkova, Stephen Ranshous, Lawrence Phillips
Data Sciences and Analytics Group, National Security Directorate
Background
Social Media (SM) is known for multi-cultural and multilingual interactions
45% of codeswitching (CS) due to lexical need
40% due to the choice of a topic
Intra-sentential CS: mix languages inside the tweet
Inter-sentential CS: mix languages across tweets
Why? To express thoughts or feelings, to address a different audience, to attract attention, to emphasize a point
Data
Multilingual user timelines via public Twitter API 09/15–01/16
27,335 users, 12 Non-English (NE) languages
6,036,085 tweets: 3,718,212 (EN) and 2,317,873 (NE)
L1 identification: ESL essays vs. informal tweets
Cross-lingual transfer: the influence of NE languages on various levels of linguistic performance in EN
Tweet IDs, user IDs and language enrichments available at
Approach
Predictive signals: lexical (words, chars), semantic (embeddings), syntactic (PoS tags), stylistic (interactions)
ADABOOST LOGREG RANDFOREST
Deep learning model input:
Content: word, character, and byte representations
Graph: interactions as vectors
Content + Graph: the above combination
Content + Transfer: embeddings initialized using transfer learning (large Twitter dataset)
Cross-Lingual Analysis: Syntax and Style
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Language Complexity
Tweet length and punctuation: Hindi↑ vs. Polish↓, French↓
Language Subjectivity
Emoticons: Tagalog↑
Repeated punctuation: Russian↑ vs. Polish↓
Elongations: German↑ vs. Russian↓
Communication Behavior
Hashtags: German↑, French↑, Hindi↓, Korean↓
Semantic: Embeddings
Twitter Glove
0.53
0.47
0.57
Twitter NPMI
0.55
0.52
News W2V
0.54
0.46
0.56
Twitter W2V
0.58
0.67
Lexical: Words vs. Characters
Word 3-grams
0.72
0.65
Char 3-grams
0.42
0.60
0.59
Char 5-grams
0.64
0.66
Syntactic and Stylistic
Profile
0.48
0.41
Style
Syntax
Task: Predict foreign languages users speak exclusively from their English tweets
Hypothesis: Lexical, semantic, syntactic and stylistic choices in English content have different predictive power on inferring non-English languages users speak on social media
URLs: Russian↑ vs. Hindi↓
Mentions: Portuguese↑, Italian↑, Hindi↑ vs. German↓
Syntactic Analysis
Adjectives: Hindi↑ vs. German↓, Polish↓
Determiners: Hindi↑, Korean↑ vs. German↓, Polish↓
Nouns: Russian↑ vs. Polish↓
Prepositions: Hindi↑ vs. Polish↓, Tagalog↓
Pronouns: Korean↑, Portuguese↑ vs. Tagalog↑, German↓
Adverbs: Korean↑ vs. German↓, Polish↓
Conjunctions and verbs: Korean↑, Hindi↑ vs. German↓, Polish↓
ABOUT
Pacific Northwest
National Laboratory
The Pacific Northwest National Laboratory,
located in southeastern Washington State,
is a U.S. Department of Energy Office of
Science laboratory that solves complex
problems in energy, national security, and
the environment, and advances scientific
frontiers in the chemical, biological, materials,
environmental, and computational sciences.
The Laboratory employs nearly 5,000 staff
members, has an annual budget in excess
of $1 billion, and has been managed by
Ohio-based Battelle since 1965.
For more information on the science
you see here, please contact:
Svitlana Volkova
Senior Scientist
Pacific Northwest National Laboratory
Richland, WA 99354
(509)
ABOUT
Pacific Northwest
National Laboratory
The Pacific Northwest National Laboratory,
located in southeastern Washington State,
is a U.S. Department of Energy Office of
Science laboratory that solves complex
problems in energy, national security, and
the environment, and advances scientific
frontiers in the chemical, biological, materials,
environmental, and computational sciences.
The Laboratory employs nearly 5,000 staff
members, has an annual budget in excess
of $1 billion, and has been managed by
Ohio-based Battelle since 1965.
For more information on the science
you see here, please contact:
Staff Name
Pacific Northwest National Laboratory
P.O. Box 999, MS-IN: X#-##
Richland, WA 99352
(509) 37X-XXXX
27K users
6M tweets
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