Extracting Entities of Interest from Comparative Product Reviews

This paper presents a deep learning based approach to extract product comparison information out of user reviews on various e-commerce websites. Any comparative product review has three major entities of information: the names of the products being compared, the user opinion (predicate) and the feature or aspect under comparison. All these informing entities are dependent on each other and bound by the rules of the language, in the review. We observe that their inter-dependencies can be captured well using LSTMs. We evaluate our system on existing manually labeled datasets and observe out-performance over the existing Semantic Role Labeling (SRL) framework popular for this task.Comment: Source Code: https://github.com/jatinarora2702/Review-Information-Extractio

Exploration and visualization of gene expression with neuroanatomy in the adult mouse brain

<p>Abstract</p> <p>Background</p> <p>Spatially mapped large scale gene expression databases enable quantitative comparison of data measurements across genes, anatomy, and phenotype. In most ongoing efforts to study gene expression in the mammalian brain, significant resources are applied to the mapping and visualization of data. This paper describes the implementation and utility of Brain Explorer, a 3D visualization tool for studying <it>in situ </it>hybridization-based (ISH) expression patterns in the Allen Brain Atlas, a genome-wide survey of 21,000 expression patterns in the C57BL6J adult mouse brain.</p> <p>Results</p> <p>Brain Explorer enables users to visualize gene expression data from the C57Bl/6J mouse brain in 3D at a resolution of 100 μm³, allowing co-display of several experiments as well as 179 reference neuro-anatomical structures. Brain Explorer also allows viewing of the original ISH images referenced from any point in a 3D data set. Anatomic and spatial homology searches can be performed from the application to find data sets with expression in specific structures and with similar expression patterns. This latter feature allows for anatomy independent queries and genome wide expression correlation studies.</p> <p>Conclusion</p> <p>These tools offer convenient access to detailed expression information in the adult mouse brain and the ability to perform data mining and visualization of gene expression and neuroanatomy in an integrated manner.</p

TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection

Punctuation and Segmentation are key to readability in Automatic Speech Recognition (ASR), often evaluated using F1 scores that require high-quality human transcripts and do not reflect readability well. Human evaluation is expensive, time-consuming, and suffers from large inter-observer variability, especially in conversational speech devoid of strict grammatical structures. Large pre-trained models capture a notion of grammatical structure. We present TRScore, a novel readability measure using the GPT model to evaluate different segmentation and punctuation systems. We validate our approach with human experts. Additionally, our approach enables quantitative assessment of text post-processing techniques such as capitalization, inverse text normalization (ITN), and disfluency on overall readability, which traditional word error rate (WER) and slot error rate (SER) metrics fail to capture. TRScore is strongly correlated to traditional F1 and human readability scores, with Pearson's correlation coefficients of 0.67 and 0.98, respectively. It also eliminates the need for human transcriptions for model selection

Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead

Segmentation for continuous Automatic Speech Recognition (ASR) has traditionally used silence timeouts or voice activity detectors (VADs), which are both limited to acoustic features. This segmentation is often overly aggressive, given that people naturally pause to think as they speak. Consequently, segmentation happens mid-sentence, hindering both punctuation and downstream tasks like machine translation for which high-quality segmentation is critical. Model-based segmentation methods that leverage acoustic features are powerful, but without an understanding of the language itself, these approaches are limited. We present a hybrid approach that leverages both acoustic and language information to improve segmentation. Furthermore, we show that including one word as a look-ahead boosts segmentation quality. On average, our models improve segmentation-F0.5 score by 9.8% over baseline. We show that this approach works for multiple languages. For the downstream task of machine translation, it improves the translation BLEU score by an average of 1.05 points

Single cell fertilizer (SCF): Evidence to prove that bio-molecules are potent nutrient for plant growth

Fertilizers of various kinds are used for the cultivation of crop plants for hyper production of plant based food materials. The study used bio-molecules made in a bacterial cell. The experimental results showed tremendous effect on plant growth. These cellular molecules were made by treating the bacterial cells with lysozyme and protenase K. The wet/weight was increased in multiple folds compared to that of control sets. The fold of increase was 4.79 for rice, 2.77 for wheat, 1.89 for gram and 1.89 for pea when bacterial cellular molecules were used as fertilizer

An anatomic gene expression atlas of the adult mouse brain

Studying gene expression provides a powerful means of understanding structure-function relationships in the nervous system. The availability of genome-scale in situ hybridization datasets enables new possibilities for understanding brain organization based on gene expression patterns. The Anatomic Gene Expression Atlas (AGEA) is a new relational atlas revealing the genetic architecture of the adult C57Bl/6J mouse brain based on spatial correlations across expression data for thousands of genes in the Allen Brain Atlas (ABA). The AGEA includes three discovery tools for examining neuroanatomical relationships and boundaries: (1) three-dimensional expression-based correlation maps, (2) a hierarchical transcriptome-based parcellation of the brain and (3) a facility to retrieve from the ABA specific genes showing enriched expression in local correlated domains. The utility of this atlas is illustrated by analysis of genetic organization in the thalamus, striatum and cerebral cortex. The AGEA is a publicly accessible online computational tool integrated with the ABA (http://mouse.brain-map.org/agea)

Mechanistic Insight into the Reactivation of BCAII Enzyme from Denatured and Molten Globule States by Eukaryotic Ribosomes and Domain V rRNAs

In all life forms, decoding of messenger-RNA into polypeptide chain is accomplished by the ribosome. Several protein chaperones are known to bind at the exit of ribosomal tunnel to ensure proper folding of the nascent chain by inhibiting their premature folding in the densely crowded environment of the cell. However, accumulating evidence suggests that ribosome may play a chaperone role in protein folding events in vitro. Ribosome-mediated folding of denatured proteins by prokaryotic ribosomes has been studied extensively. The RNA-assisted chaperone activity of the prokaryotic ribosome has been attributed to the domain V, a span of 23S rRNA at the intersubunit side of the large subunit encompassing the Peptidyl Transferase Centre. Evidently, this functional property of ribosome is unrelated to the nascent chain protein folding at the exit of the ribosomal tunnel. Here, we seek to scrutinize whether this unique function is conserved in a primitive kinetoplastid group of eukaryotic species Leishmania donovani where the ribosome structure possesses distinct additional features and appears markedly different compared to other higher eukaryotic ribosomes. Bovine Carbonic Anhydrase II (BCAII) enzyme was considered as the model protein. Our results manifest that domain V of the large subunit rRNA of Leishmania ribosomes preserves chaperone activity suggesting that ribosome-mediated protein folding is, indeed, a conserved phenomenon. Further, we aimed to investigate the mechanism underpinning the ribosome-assisted protein reactivation process. Interestingly, the surface plasmon resonance binding analyses exhibit that rRNA guides productive folding by directly interacting with molten globule-like states of the protein. In contrast, native protein shows no notable affinity to the rRNA. Thus, our study not only confirms conserved, RNA-mediated chaperoning role of ribosome but also provides crucial insight into the mechanism of the process

Predicting Group Success in Meetup

Success of groups in Meetup is of utmost importance for members who organize them. However, measures of group success in Meetup is quite vague till now. In this paper, we take a step to quantify the success of Meetup groups. Driven by a comprehensive study of our Meetup dataset, we handpick a set of key properties which can potentially regulate a group’s success. Finally, we develop a machine learning model leveraging on these features which can predict success of Meetup groups early with high accuracy

On the Role of Micro-categories to Characterize Event Popularity in Meetup

Event-based social networking platforms such as Meetup have recently witnessed a huge growth. However, with the rise in the volume of groups and events, making individual events attractive has become increasingly challenging for its organizers. As a result, we find that events hosted by groups of same category at similar venues and similar times, also widely differ in their popularity. Data study reveals that the topics specified in textual descriptions of events may be key to their popularity. In this paper, we introduce a novel concept of topical micro-categories in the context of EBSNs for accurately characterizing events, such that events belonging to the same micro-category exhibit similar popularity profile. We develop a principled method to detect such micro-categories from the textual descriptions of individual events. Our experiments reveal the significance of the detected micro-categories in determining the popularity of associated Meetup events and groups. We also investigate the effectiveness of the micro-categories in a real-world application scenario by developing a recommendation model; this model recommends relevant micro-categories to a group for hosting its future events with enhanced popularity. Notably, our model achieves an average NDCG score of around 0.75 showing a straight 5% improvement over the best performing competing method

On the Splitting Dynamics of Meetup Social Groups

Groups in online social networks witness continuous evolution by loss of existing members and gain of new members. In this paper, we present a study of group split in Meetup, where a major fraction of members leave the existing group together and join a newly formed group. We identify pivotal group members, called splitters, playing key roles in group split by influencing the existing members to leave the group. We provide an in-depth analysis of the empirical data to reveal key motivating factors leading to a group split and its subsequent impact. Finally, we develop a prediction model for early detection of splitters, as well as the group members likely to be influenced by the splitter to leave the group