Model-Portability Experiments for Textual Temporal Analysis
نویسندگان
چکیده
We explore a semi-supervised approach for improving the portability of time expression recognition to non-newswire domains: we generate additional training examples by substituting temporal expression words with potential synonyms. We explore using synonyms both from WordNet and from the Latent Words Language Model (LWLM), which predicts synonyms in context using an unsupervised approach. We evaluate a state-of-the-art time expression recognition system trained both with and without the additional training examples using data from TempEval 2010, Reuters and Wikipedia. We find that the LWLM provides substantial improvements on the Reuters corpus, and smaller improvements on the Wikipedia corpus. We find that WordNet alone never improves performance, though intersecting the examples from the LWLM and WordNet provides more stable results for Wikipedia.
منابع مشابه
Temporal-Textual Retrieval: Time and Keyword Search in Web Documents
As the web ages, many web documents become relevant only to certain time periods, such as web-pages containing news and events or those documenting natural phenomena. Hence, to retrieve the most relevant pages, in addition to providing the relevant keywords, one may desire to identify the relevant time period(s) as well, e.g., “Barack Obama 1980-1985”. Unfortunately, not much work has been done...
متن کاملExtraction of model parameters for reactive solute transport
In the present study, tried to examine the reactive solute relationships for transport and degradation processes through the rockfill media. By applying the analytical solution of reactive transport, the 1st to 3rd theoretical temporal moments have been extracted then, by using two methods of curve fitting and temporal moment matching, the coefficients of dispersion and degradation have been ex...
متن کاملHawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter
Classification of temporal textual data sequences is a common task in various domains such as social media and the Web. In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information. Our experiments on rumour stance classification on four Twitter datasets show the importance of using the temporal informat...
متن کاملTemporal and Social Context Based Burst Detection from Folksonomies
Burst detection is an important topic in temporal stream analysis. Usually, only the textual features are used in burst detection. In the theme extraction from current prevailing social media content, it is necessary to consider not only textual features but also the pervasive collaborative context, e.g., resource lifetime and user activity. This paper explores novel approaches to combine multi...
متن کاملIndexing temporal information for web pages
Temporal information plays important roles in Web search, as Web pages intrinsically involve crawled time and most Web pages contain time keywords in their content. How to integrate temporal information in Web search engines has been a research focus in recent years, among which some key issues such as temporal-textual indexing and temporal information extraction have to be first studied. In th...
متن کامل