natural language processing
play

Natural Language Processing - PDF document

Course Information Natural Language Processing http://www.cs.berkeley.edu/~klein/cs288/fa14/ https://piazza.com/berkeley/fall2014/cs288/ Lecture 1: Introduction Dan Klein UC Berkeley Course Requirements Other Announcements Course


  1. Course Information Natural Language Processing http://www.cs.berkeley.edu/~klein/cs288/fa14/ https://piazza.com/berkeley/fall2014/cs288/ Lecture 1: Introduction Dan Klein – UC Berkeley Course Requirements Other Announcements � Course Contacts: � Prerequisites: � CS 188 (CS 281a) and preferably CS170 (A-level mastery) � Webpage: materials and announcements � Strong skills in Java or equivalent � Piazza: discussion forum � Deep interest in language � Successful completion of the first project � Enrollment: We’ll try to take everyone who meets the � There will be a lot of math and programming requirements � Work and Grading: � Six assignments (individual, jars + write-ups) � Computing Resources � This course is a major time-commitment! � You will want more compute power than the instructional labs � Experiments can take up to hours, even with efficient code � Books: � Primary text: Jurafsky and Martin, Speech and Language � Recommendation: start assignments early Processing, 2 nd Edition (not 1 st ) � Also: Manning and Schuetze, Foundations of Statistical NLP � Questions? ���������������������� Language Technologies Goal: Deep Understanding Reality: Shallow Matching � � Requires context, linguistic Requires robustness and scale structure, meanings… � Amazing successes, but fundamental limitations Source: Slav Petrov �

  2. Speech Systems Example: Siri � Siri contains � Automatic Speech Recognition (ASR) � Speech recognition � Audio in, text out � SOTA: 0.3% error for digit strings, 5% dictation, 50%+ TV � Language analysis � Dialog processing � Text to speech ������������ � Text to Speech (TTS) � Text in, audio out � SOTA: totally intelligible (if sometimes unnatural) Image: Wikipedia Text Data is Superficial … But Language is Complex �� ������� �� � ����� ����� �� �� ������� �� � ����� ����� �� ���������� ��� ���� ��� ���������� ��� ���� ��� ������ ��� ��� � ����!��� �� ������� �� ��� ������ ��� ��� � ����!��� �� ������� �� ��� ����� ��� ����� ��� �� �������� �� ���� �����" �� �������� �� ���� �����" � Semantic structures � References and entities � Discourse-level connectives � Meanings and implicatures � Contextual factors � Perceptual grounding � … Deeper Linguistic Analysis Learning Hidden Syntax ����������������������� ����� �� ���� ��� ����� �� �� ���� ����� �� �� � ��� �������������� ������ ���� ���� �� �� ������ !��� ��"��� !���� ����� !� #� $� ������������������������������������������������������������������ ����� %��� �����&� ������ ��������������������������������������������������������������������� � �����' ��( ��� )��� ������!��������������������"���#�����������������������$ ����� *��+ ,�������� ������ ���#���$��%&' �

  3. Search, Facts, and Questions Example: Watson Summarization Language Comprehension? � Condensing documents � Single or multiple docs � Extractive or synthetic � Aggregative or representative � Very context- dependent! � An example of analysis with generation Machine Translation � Translate text from one language to another � Recombines fragments of example translations � Challenges: � What fragments? [learning to translate] � How to make efficient? [fast translation search] � Fluency (next class) vs fidelity (later) (

  4. Data By Itself Isn’t Enough! More Data: Machine Translation *�����������#������#������#�����������������+#����� ����������� �123*4 ����#����,���� ��,�#����������,�-���#���������������" .������#��������������� ����#�������������#��� ������������������� 526�7 ���������������������������������������������� �" /����0�/�������#�����0�/����������0�/�����������0�/���0�/��� �������0� �8���.� /��������0�/��0�/��� �0�/��0�/�0�/�������0�/��0�/-��#�0�/�������������0�/"0 /��0��/��#��0�/�����#����0�/������������0�/�����0�/��#��0�/��0�/����0� �&8���.� /��0�/��� 0�/����0�/�������0�/��0�/-��#�0�/�������0�/"0 /����0�/��#�����0�/������������������#����0�/��������#��0�/�������0�/�� �&&8���.� �������0�/������$��������0�/"0 /�������#�����0�/������������������#����0�/��������#��0�/�-���#���$� �&&&8���.� �������0�/�����������������0�/"0 Data and Knowledge � Classic knowledge representation worry: How will a machine ever know that… � Ice is frozen water? � Beige looks like this: � Chairs are solid? � Answers: � 1980: write it all down � 2000: get by without it � 2020: learn it from data Names vs. Entities Deeper Understanding: Reference )

  5. Example Errors Discovering Knowledge Grounded Language Grounding with Natural Data ������������������������ What is Nearby NLP? Example: NLP Meets CL � Computational Linguistics � Using computational methods to learn more about how language works � We end up doing this and using it � Cognitive Science � Figuring out how the human brain works � Includes the bits that do language � Humans: the only working NLP prototype! � Speech Processing � Mapping audio signals to text � Traditionally separate from NLP, converging? � Two components: acoustic models and language � Example: Language change, reconstructing ancient forms, phylogenies models � Language models in the domain of stat NLP … just one example of the kinds of linguistic models we can build 9

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend


More recommend