The Cambridge Grammar of the English Language

With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.

A survey on opinion mining and sentiment analysis

https://www.bmj.com/content/bmj/1/5125/836.2.full.pdf

Dictionary of psychology

Thank you very much for reading sequence organization in interaction a primer in conversation analysis. Maybe you have knowledge that, people have look numerous times for their chosen novels like this sequence organization in interaction a primer in conversation analysis, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some infectious virus inside their laptop.

Sequence Organization In Interaction A Primer In Conversation Analysis

/pdf/yeongeoyi-neunggyeogseong-ergativity-2el2396dgf.pdf

영어의 능격성(Ergativity)

We describe a new dependency parser for English tweets, TWEEBOPARSER. The parser builds on several contributions: new syntactic annotations for a corpus of tweets (TWEEBANK), with conventions informed by the domain; adaptations to a statistical parsing algorithm; and a new approach to exploiting out-of-domain Penn Treebank data. Our experiments show that the parser achieves over 80% unlabeled attachment accuracy on our new, high-quality test set and measure the benefit of our contributions.
Our dataset and parser can be found at http://www.ark.cs.cmu.edu/TweetNLP.

/pdf/a-dependency-parser-for-tweets-487z80ulqo.pdf

A Dependency Parser for Tweets

We discuss the phenomenon of closest conjunct agreement with a special focus on head-final languages; we present data from two such languages, Hindi and Tsez, which allow agreement with the rightmost conjunct when the verb follows the conjoined phrase. This contrasts with head-initial languages, such as Arabic, where close conjunct agreement is with the leftmost conjunct in clauses with VS order. In addition, both languages exhibit certain flexibility of word order at root clause level; when the verb precedes the conjoined phrase, it can also agree with the leftmost conjunct. The empirical data from the two languages raise the following questions. First, is the typological difference between head-initial and head-final languages in the context of coordination due to a difference in the structure of coordination in these two groups? Second, to what extent is the syntactic configuration relevant to the computation of closest conjunct agreement? Third, what is the role of linear proximity in closest conjunct agreement? These questions have wider implications for the analysis of agreement and the relation between syntax and the morpho-phonological component.

/pdf/closest-conjunct-agreement-in-head-final-languages-50qxjmdjcl.pdf

Closest Conjunct Agreement in Head-Final Languages

Recent research has identified several vulnerable areas in heritage language grammars, among which morphosyntax is among the most affected. In this study, we report on the morphosyntactic competence of Hindi heritage speakers living in the U.S and show that these speakers have representational problems with ergative, accusative and dative case morphology, albeit to different degrees. Hindi is a split ergative language with a complex interaction of case and agreement. Transitive predicates in perfective aspect co-occur with subjects marked with ergative case (-ne) and object agreement. Animate specific direct objects are marked with the particle -ko, and so are the indirect objects and dative subjects. 21 Hindi native speakers and 28 Hindi heritage speakers completed a sociolinguistic questionnaire, a Hindi cloze test, an oral narrative task and a bimodal acceptability judgment task. The results showed significant differences between the fluent native speakers and the heritage speakers on all measures.

Erosion of case and agreement in Hindi heritage speakers

In this paper, we have addressed the task of PropBank annotation of light verb constructions, which like multi-word expressions pose special problems To arrive at a solution, we have evaluated 3 different possible methods of annotation The final method involves three passes: (1) manual identification of a light verb construction, (2) annotation based on the light verb construction's Frame File, and (3) a deterministic merging of the first two passes We also discuss how in various languages the light verb constructions are identified and can be distinguished from the non-light verb word groupings

/pdf/propbank-annotation-of-multilingual-light-verb-constructions-3adlarhu3r.pdf

PropBank Annotation of Multilingual Light Verb Constructions

We develop a supersense taxonomy for adjectives, based on that of GermaNet, and apply it to English adjectives in WordNet using human annotation and supervised classification. Results show that accuracy for automatic adjective type classification is high, but synsets are considerably more difficult to classify, even for trained human annotators. We release the manually annotated data, the classifier, and the induced supersense labeling of 12,304 WordNet adjective synsets.

/pdf/augmenting-english-adjective-senses-with-supersenses-2l4w3f8jtt.pdf

Archna Bhatia

Papers

A Dependency Parser for Tweets

Closest Conjunct Agreement in Head-Final Languages

Erosion of case and agreement in Hindi heritage speakers

PropBank Annotation of Multilingual Light Verb Constructions

Augmenting English Adjective Senses with Supersenses