Interpreting comparative constructions in biomedical text

Fiszman, Marcelo; Demner‐Fushman, Dina; Lang, F.M.; Goetz, Philip; Rindflesch, Thomas C.

doi:10.3115/1572392.1572417

Cited by 60 publications

(39 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the annotator also annotated an additional 50 abstracts, which was used in the development of the comparison patterns. Although the work by Fiszman et al (2007) attempts to tackle the similar task of identifying comparison sentences and its components, we do not directly compare with their results. This is due to the fact that their implementation is limited to "direct comparisons of the pharmacological actions of two drugs".…”

Section: Discussionmentioning

confidence: 99%

“…They reported an average F-score of 0.78 for identifying the compared drug names, scale and scale position. To the best of our knowledge, (Fiszman et al, 2007) is the only reported work that goes beyond identification of comparison sentences to identify the different components of the comparison in biomedical text. But unlike our work, theirs is limited to comparison between drugs, does not extract the comparison aspect and appears to be limited in their coverage of comparison structures.…”

Section: Related Workmentioning

confidence: 99%

“…The focus of this work was on identifying comparison sentences and the extraction of its components was not addressed. Fiszman et al (2007) described a technique to identify comparative constructions in MEDLINE citations using under-specified semantic interpretation. The authors used textual patterns combined with semantic predications extracted from the semantic processor SemRep (Rindflesch and Fiszman, 2003;Rindflesch et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Identifying Comparative Structures in Biomedical Text

Gupta¹,

Mahmood²,

Ross³

et al. 2017

BioNLP 2017

View full text Add to dashboard Cite

Comparison sentences are very commonly used by authors in biomedical literature to report results of experiments. In such comparisons, authors typically make observations under two different scenarios. In this paper, we present a system to automatically identify such comparative sentences and their components i.e. the compared entities, the scale of the comparison and the aspect on which the entities are being compared. Our methodology is based on dependencies obtained by applying a parser to extract a wide range of comparison structures. We evaluated our system for its effectiveness in identifying comparisons and their components. The system achieved a F-score of 0.87 for comparison sentence identification and 0.77-0.81 for identifying its components.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Comparative Structures in Biomedical Text

Gupta¹,

Mahmood²,

Ross³

et al. 2017

BioNLP 2017

View full text Add to dashboard Cite

show abstract

“…Additional details can be found in [29]. In [26], Fiszman et al studied the problem of identifying which object has more of certain features in comparative sentences in biomedical texts, but it does not analyze opinions.…”

Section: Context-dependent Comparative Opinion Wordsmentioning

confidence: 99%

“…Due to its tremendous value for practical applications, there has been an explosive growth of both research in academia and applications in the industry. There are now at least [20][21][22][23][24][25][26][27][28][29][30] companies that offer sentiment analysis services in USA alone. This chapter introduces this research field.…”

mentioning

confidence: 99%

The Customer is Always Right: Sentiment Analysis for Bank Service Quality

Brunova¹,

Bidulya²

2017

TSU Herald. Hum Res. Humanitates

View full text Add to dashboard Cite

Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people's sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people's positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others' opinions. This is not only true for individuals but also true for organizations.One of the main reasons for the lack of study on opinions is the fact that there was little opinionated text available before the World Wide Web. Before the Web, when an individual needed to make a decision, he/she typically asked for opinions from friends and families. When an organization wanted to find the opinions or sentiments of the general public about its products and services, it conducted opinion polls, surveys, and focus groups. However, with the Web, especially with the explosive growth of the usergenerated content on the Web in the past few years, the world has been transformed.The Web has dramatically changed the way that people express their views and opinions. They can now post reviews of products at merchant sites and express their views on almost anything in Internet forums, discussion groups, and blogs, which are collectively called the user-generated content. This online wordof-mouth behavior represents new and measurable sources of information with many practical applications. Now if one wants to buy a product, he/she is no longer limited to asking his/her friends and families because there are many product reviews on the Web which give opinions of existing users of the product. For a company, it may no longer be necessary to conduct surveys, organize focus groups or employ external consultants in order to find consumer opinions about its products and those of its competitors because the user-generated content on the Web can already give them such information.However, finding opinion sources and monitoring them on the Web can still be a formidable task because there are a large number of diverse sources, and each source may also have a huge volume of opinionated text (text with opinions or sentiments). In many cases, opinions are hidden in long forum posts and blogs. It is difficult for a human reader to find relevant sources, extract related sentences with opinions, read them, summarize them, and organize them into usable forms. Thus, automated opinion discovery and summarization systems are needed. Sentiment an...

show abstract

Comparative opinion mining: A review

Varathan

Giachanou

Crestani

2016

Asso for Info Science & Tech

View full text Add to dashboard Cite

Opinion mining refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyse the public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining that deals with identifying and extracting information that is expressed in a comparative form (e.g. "paper X is better than the Y").Comparative opinion mining plays a very important role when ones tries to evaluate something, as it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles.One from perspective of techniques and the other from perspective of comparative opinion elements. It also incorporates preprocessing tools as well as dataset that were used by the past researchers that can be useful to the future researchers in the field of comparative opinion mining.

show abstract

Interpreting comparative constructions in biomedical text

Cited by 60 publications

References 24 publications

Identifying Comparative Structures in Biomedical Text

Identifying Comparative Structures in Biomedical Text

The Customer is Always Right: Sentiment Analysis for Bank Service Quality

Comparative opinion mining: A review

Contact Info

Product

Resources

About