Building a Spanish lexicon for corpus analysis

This paper seeks to describe the creation of a Spanish lexicon with semantic annotation in order to analyse more extensive corpora in the Spanish language. The semantic resources most employed nowadays are WordNet, FrameNet, PDEV and USAS, but they have been used mainly for English language research. The creation of a large Spanish lexicon will permit a greater amount of studies of corpora in Spanish can be undertaken. In the description of the steps followed for the construction of the lexicon, the difficulties encountered in its creation, and the solutions used to overcome them will be described. Finally, the construction of the lexicon will allow specific research tasks to be carried out, such as metaphor analysis, ACD studies and even PLN studies

Building a Spanish lexicon for corpus analysis

This paper seeks to describe the creation of a Spanish lexicon with semantic annotation in order to analyse more extensive corpora in the Spanish language. The semantic resources most employed nowadays are WordNet, FrameNet, PDEV and USAS, but they have been used mainly for English language research. The creation of a large Spanish lexicon will permit a greater amount of studies of corpora in Spanish can be undertaken. In the description of the steps followed for the construction of the lexicon, the difficulties encountered in its creation, and the solutions used to overcome them will be described. Finally, the construction of the lexicon will allow specific research tasks to be carried out, such as metaphor analysis, ACD studies and even PLN studies

CDH11 inhibits proliferation and invasion in head and neck cancer

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135974/1/jop12471_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135974/2/jop12471.pd

Towards A Welsh Semantic Annotation System

Automatic semantic annotation of natural language data is an important task in Natural Language Processing, and a variety of semantic taggers have been developed for this task, particularly for English. However, for many languages, particularly for low-resource languages, such tools are yet to be developed. In this paper, we report on the development of an automatic Welsh semantic annotation tool (named CySemTagger) in the CorCenCC Project, which will facilitate semantic-level analysis of Welsh language data on a large scale. Based on Lancaster’s USAS semantic tagger framework, this tool tags words in Welsh texts with semantic tags from a semantic classification scheme, and is designed to be compatible with multiple Welsh POS taggers and POS tagsets by mapping different tagsets into a core shared POS tagset that is used internally by CySemTagger. Our initial evaluation shows that the tagger can cover up to 91.78% of words in Welsh text. This tagger is under continuous development, and will provide a critical tool for Welsh language corpus and information processing at semantic level

Profiling Medical Journal Articles Using a Gene Ontology Semantic Tagger

In many areas of academic publishing, there is an explosion of literature, and sub-division of fields into subfields, leading to stove-piping where sub-communities of expertise become disconnected from each other. This is especially true in the genetics literature over the last 10 years where researchers are no longer able to maintain knowledge of previously related areas. This paper extends several approaches based on natural language processing and corpus linguistics which allow us to examine corpora derived from bodies of genetics literature and will help to make comparisons and improve retrieval methods using domain knowledge via an existing gene ontology. We derived two open access medical journal corpora from PubMed related to psychiatric genetics and immune disorder genetics. We created a novel Gene Ontology Semantic Tagger (GOST) and lexicon to annotate the corpora and are then able to compare subsets of literature to understand the relative distributions of genetic terminology, thereby enabling researchers to make improved connections between them

Inhibition of autophagy by 3-MA enhances IL-24-induced apoptosis in human oral squamous cell carcinoma cells

Abstract Background Interleukin-24(IL-24), also referred to as melanoma differentiation-associated gene-7(mda-7), is a unique member of the IL-10 gene family, which displays nearly ubiquitous cancer-specific toxicity. The most notable feature of IL-24 is selectively induced growth suppression and apoptosis in various cancer cells, with no harmful effects toward normal cells. Autophagy is a self-protective mechanism in many kinds of tumor cells that respond to anticancer treatment. It is reported that autophagy inhibition could enhance the effects of many kinds of anticancer treatments, including gene therapy. However, whether IL-24 is effective to treat oral squamous cell carcinomas (OSCC) and if autophagy inhibition could improve the anticancer effect of IL-24 towards OSCC is has not been detected. Methods MTT assays were carried out to determine the cell proliferation; Transfection was used to gene transfer; Western Blot was performed to detect the protein level of LC3II, P62, Beclin 1, Cleaved caspase-3, β-Tubulin and β-actin; Apoptosis rates and cell cycle alteration were analyzed using flow cytometry; Autophagy induction was confirmed by MDC staining, GFP-LC3 staining and transmission electron microscopy. Amount of IL-24 in the culture medium was quantified by ELISA. Apoptosis in vivo was analyzed by TUNEL assay. HE staining was used to observe the morphology of the samples. Results In the present study, we proved that IL-24 have a novel anticancer effect towards KB cells and that autophagy inhibition could improve the anticancer effect of IL-24. IL-24 treated cells showed autophagy characteristics and autophagy inhibition by 3-methyladenine (3-MA) significantly enhanced IL-24-induced apoptosis. Similar results were obtained in the KB cells xenograft tumor model. Conclusions These results suggest that the combination of autophagy inhibitors and IL-24 based on the AdLTR2EF1α-mediated gene transfer could be a promising way to cure OSCC.http://deepblue.lib.umich.edu/bitstream/2027.42/113230/1/13046_2015_Article_211.pd

A time-sensitive historical thesaurus-based semantic tagger for deep semantic annotation

Automatic extraction and analysis of meaning-related information from natural language data has been an important issue in a number of research areas, such as natural language processing (NLP), text mining, corpus linguistics, and data science. An important aspect of such information extraction and analysis is the semantic annotation of language data using a semantic tagger. In practice, various semantic annotation tools have been designed to carry out different levels of semantic annotation, such as topics of documents, semantic role labeling, named entities or events. Currently, the majority of existing semantic annotation tools identify and tag partial core semantic information in language data, but they tend to be applicable only for modern language corpora. While such semantic analyzers have proven useful for various purposes, a semantic annotation tool that is capable of annotating deep semantic senses of all lexical units, or all-words tagging, is still desirable for a deep, comprehensive semantic analysis of language data. With large-scale digitization efforts underway, delivering historical corpora with texts dating from the last 400 years, a particularly challenging aspect is the need to adapt the annotation in the face of significant word meaning change over time. In this paper, we report on the development of a new semantic tagger (the Historical Thesaurus Semantic Tagger), and discuss challenging issues we faced in this work. This new semantic tagger is built on existing NLP tools and incorporates a large-scale historical English thesaurus linked to the Oxford English Dictionary. Employing contextual disambiguation algorithms, this tool is capable of annotating lexical units with a historically-valid highly fine-grained semantic categorization scheme that contains about 225,000 semantic concepts and 4,033 thematic semantic categories. In terms of novelty, it is adapted for processing historical English data, with rich information about historical usage of words and a spelling variant normalizer for historical forms of English. Furthermore, it is able to make use of knowledge about the publication date of a text to adapt its output. In our evaluation, the system achieved encouraging accuracies ranging from 77.12% to 91.08% on individual test texts. Applying time-sensitive methods improved results by as much as 3.54% and by 1.72% on average

Reversing the polarity with emoticons

Technology advancement in social media software allows users to include elements of visual communication in textual settings. Emoticons are widely used as visual representations of emotion and body expressions. However, the assignment of values to the “emoticons” in current sentiment analysis tools is still at a very early stage. This paper presents our experiments in which we study the impact of positive and negative emoticons on the classifications by fifteen different sentiment tools. The “smiley” :) and the “sad” emoticon :( and raw-text are compared to verify the degrees of sentiment polarity levels. Questionnaires were used to collect human ratings of the positive and negative values of a set of sample comments that end with these emoticons. Our results show that emoticons used in sentences are able to reverse the polarity of their true sentiment values