The Master Thesis Search Engine Optimization service is an effective solution for those customers seeking excellent writing quality for less money. We guarantee % confidentiality and anonymity. / FREE UNLIMITED REVISIONS. If you are not fully satisfied with your paper, ask us for a free revision within Sep 13, · The work constitutes part of the master thesis of Oleg Borisov at the Universitá della Svizzera italiana (USI) on conversational search. References Radlinski and Craswell [] F. Radlinski, N. Craswell, A theoretical framework for conversational search, in: CHIIR, ACM, , pp. – Master Thesis Search Engine Optimization research papers, speeches, book reviews, and other custom task completed Master Thesis Search Engine Optimization by our writers are both of high quality and cheap. It is surprising, but we do have some tricks to lower prices without hindering quality/10()
Recent research master thesis search engine optimization shown that mixed-initiative conversational search, based on the interaction between users and computers to clarify and improve a query, provides enormous advantages.
Nonetheless, incorporating additional information provided by the user from the conversation poses some challenges. In fact, further interactions could confuse the system as a user might use words irrelevant to the information need but crucial for correct sentence construction in the context of multi-turn conversations. To this aim, in this paper, we have collected two conversational keyword extraction datasets and propose an end-to-end document retrieval pipeline incorporating them.
Furthermore, we study the performance of two neural keyword extraction models, namely, BERT and sequence to sequence, in terms of extraction accuracy and human annotation. Finally, we study the effect of keyword extraction on the end-to-end neural IR performance and show that our approach beats state-of-the-art IR models.
We make the two datasets publicly available to foster research in this area. Oleg Borisov. Mohammad Aliannejadi. Fabio Crestani. Open-domain conversational search assistants aim at answering user quest Recent advances in conversational systems have changed the search paradi Passage retrieval in a conversational context is essential for many down Recently, neural approaches to spoken content retrieval have become popu Frequently-Asked-Question FAQ retrieval provides an effective procedur We present CAM comparative argumentative machinea novel open-domain Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.
Recent developments in speech recognition and deep learning have led to intelligent assistants, master thesis search engine optimization, such as Google Assistant, Microsoft Cortana, and Apple Siri.
Consequently, researchers and users are exploring novel means of communication and information access, such as spoken queries and conversations. In Web search, where users usually type their queries, they take some time to formulate a query and often do not follow common sentence structures. For example, they only focus on using the most important words for their search. Consequently, a narrow focus is created for the search engine, making the inspection of documents for the most relevant query words easier.
In contrast to this, conversational IR faces challenges due to the inclination of users to follow their own speech patterns when formulating queries rhetorically. Here, users tend to include some unnecessary terms that appear crucial for a proper sentence construction but might derail the IR model in searching for relevant documents [ 13 ]. This could also be magnified when a conversation evolves into multiple turns [ 1415 ] and a new form of conversation is presented to the user, master thesis search engine optimization, such as when the system asks clarifying questions.
This happens mainly due to the context-dependence nature of multi-turn conversations and new types of responses that could emerge in a mixed-initiative conversation.
While the effectiveness of conversational systems has been studied before [ 7168master thesis search engine optimization, 17 ]the main goal of this paper is to study if the identification of keywords retrieved from the human-computer interaction will help achieve better retrieval results. To this aim, we collect two datasets of keyword extraction and study the effectiveness of multiple generative models on them. Our first dataset is collected based on the performance of the retrieval model using different keywords, while the other is collected from news articles online.
Every news article comes with a title and a set of keywords. Our intuition is that a neural model can learn to extract useful keywords from news titles and use this external knowledge for more effective keyword extraction in a conversation.
We study the effect of various keyword extraction strategies on non-neural and neural document retrieval pipelines. To the best of our knowledge, keyword extraction in the context of mixed-initiative conversational IR has not been studied before. In our retrieval pipeline, after the conversational phase, where the system interacts with the user to clarify the query ambiguities, the conversational sequence is passed to the keyword extractor, identifying the most important terms from the sentences.
In parallel to that, the document retrieval model performs the first relevance ranking of the documents based on the original conversation. Finally, the Neural IR performs re-ranking using top documents from the IR phase 1 and keywords obtained by keyword extractor from Keyword Extraction Phase as inputs of the system, master thesis search engine optimization.
As the topics addressed by this study have only recently surfaced in research, a substantial amount of work needed to be done to answer whether keywords could support the IR model with document retrieval tasks.
As was discussed earlier, keyword extraction from short-sized documents using Deep Learning is a relatively new topic. The previously created Inspec, SemEval, SemEval datasets are not suitable for this research, as they are focused on keyword and keyphrase extraction from medium- and large-sized texts e.
In contrast master thesis search engine optimization this, the main focus of this research is keyword extraction from small-sized sentences of the length of no more than 20 words, which is the average English sentence length [ 21 ]. Therefore, we collect and release two types of datasets: i News-Keyword based and ii IR-Keyword based datasets, master thesis search engine optimization. Online newspaper websites and other social network Web pages tend to follow a content structure, where common articles are structured as title, main text, tags sometimes hashtags.
Content creators try not only to select an appealing and interesting title name but also to summarize the content in one sentence, thus selecting the most important words to portray the key message of an article. Authors usually also choose some tags that either describe the article in master thesis search engine optimization most general way or place the story in the context of other related articles that one could find on the website.
Taking into consideration that writers pay very close attention to the title and master thesis search engine optimization tags used, where it is not unusual for tag words to appear in the title, brings us to the first method of keyword dataset: considering title as the input textand tags as the target keywords.
If a tag does not appear in a corresponding title, we do not add it to the keywords list as shown in Table 1. In total, overtitle-tag pairs have been obtained using this method. After filtering the outliers and the items where the tags do not appear in the title, master thesis search engine optimization, the dataset shrinks to 79, instances.
Classical IR systems only focus on the basic preprocessing of the query, master thesis search engine optimization, such as the removal of stopwords and punctuation. Having too many words could confuse the system and lead it to retrieve unwanted results, master thesis search engine optimization. Therefore, a correct keyword identification could lead to better retrieval performance, while selecting less good keywords will inevitably worsen the output results.
We developed the IR-Keyword-based dataset based on this assumption, applying the previously created Qulac dataset [ 7 ]. The main idea is to identify a set of words from qtand awhich will lead to the greatest relevance of retrieved documents. Due to the complexity of the permutations of all potential keywords of the whole set qtawe decided to focus on one component at a time.
The algorithm that was used is presented in Algorithm 1. The master thesis search engine optimization idea is to choose s 0master thesis search engine optimization, which could be a query, question, or an answer. For example, let us consider s 0 a query and s 1s 2 to represent the question and answer. Afterward, we would like to consider all possible subsets of words of s 0 query in our examplewhich will form a set of potential keywords.
In mathematical terms, such operation is known as the powerset. For instance, if s 0 is "How are you? The cardinality of a powerset highly depends on the number of words that the input sentence contains. To address having a large powerset, we limit the maximum size of the subset to four words. Next, we consider one instance k i from the potential keyword set and retrieve the documents by supplying k is 1 and s 2 to the document retrieval model.
In the end, we save the s 0 as the input text and k b e s t as the set of keywords that led to the retrieval of the most relevant documents. We repeat a similar operation by considering s 0 as the question, and s 1s 2 as query and question, and later s 0 as the answer, master thesis search engine optimization, and s 1s 2 as query and answer, respectively.
We apply the same process for all conversations from the Qulac dataset until we receive keywords from all queries, questions, and answers. Applying this approach, 15, data samples were obtained.
The benefit which this approach suggests is that where the answer of a user in a computer interaction is uncertain or ambiguous and will not provide any important information, the system learns to ignore these. In this scenario, the system should ideally ask another question or base the search only on the initial query. Therefore, the proposed method of dataset generation will be able to mimic this behavior.
This section describes our conversational IR framework. We start with the neural models that we used for the keyword extraction task. Then we continue with the neural IR models and describe how keyword extraction fits into our pipeline.
For the Keyword Extraction Phasewe experimented with two different types of neural models: Sequence-to-Sequence architecture and BERT model [ 2324 ]. Sequence-to-Sequence architecture uses Gated Recurrent Unit GRU as a recurrent neural networkmaster thesis search engine optimization, the Attention mechanism master thesis search engine optimization help the decoder, and pre-trained Word2Vec embeddings the performance on the words outside of the training set vocabulary.
We use Sequence-to-Sequence because it has been a state-of-the-art architecture for many different NLP tasks and established new benchmarks for the tasks of Neural Machine Translation.
In contrast to the previously described model, we also selected BERT as the most recently developed Master thesis search engine optimization neural architecture in the field of NLP.
Therefore, by fine-tuning the model, it is possible to achieve great results in tasks, such as: Named Entity Recognition NERSentence Classification, Answer Searching, and others. To train selected architectures, we formulate the task of keyword extraction in the form of a NER task, as shown in Table 2, master thesis search engine optimization. Where we say that master thesis search engine optimization word is a keyword it its corresponding entity is labeled as " 1 ", and not a keyword if it is marked as " 0 ".
Example of Data Supplied to Neural Networks. We extend the solution available from previous research [ 7 ] by adding Information Retrieval Phase 2represented by the Neural IR model. We study the effectiveness of the following commonly used two Neural IR models:.
Deep Relevance Matching Model DRMM : this model puts more emphasis on the relevance both semantic and lexical matching of the query rather than on exclusively semantic matching. It considers three crucial factors of the "handling of the exact matching signals, master thesis search engine optimization, query term importance, and diverse matching requirements" [ 28 ]. Deep Semantic Similarity Model DSSM : based on the Siamese network architecture, master thesis search engine optimization, DSSM has the main focus in comparing cosine similarities of the vector representations of a query and the document, where vector representations are learned using Deep fully-connected layers.
For keyword extraction experiments, we use the two datasets described in Section 2. As for extraction accuracy, we use the following evaluation metrics: Precision, Recall, Average Tag Correct Identification ATCI 10 10 10 tests the quality of the overall assigned tags, by checking if the model has correctly assigned keyword or not keyword tagand Correct per Response Fill CpRF 11 11 11 It captures the ratio of fully correct and partially correct predictions to the total amount of sentences in the dataset adopted from MUC Also, we perform a human evaluation on the extracted keywords, where we ask the human annotators to score each extracted keyword from 1 to 5.
Our IR evaluation follows the standard IR metrics, namely, Normalized Discounted Cumulative Gain at k nDCG kMaster thesis search engine optimization at k P kMean Reciprocal Rank MRRand Mean Average Precision MAP. Statistical Significance. We perform two-tailed paired t-test. To evaluate the performance of Keyword Extracting Neural Networks, two methods have been used.
The first one relies on test dataset accuracywhile the second one is a human evaluation method. Test dataset accuracy. Table 3 shows the performance of the Keyword Extractors We also created a simple "Non-Neural approach" to serve as a baseline. This method operates in a very elementary way: the word frequencies were calculated from the training dataset.
Using a brute force approach, the optimal frequency threshold was found, which maximizes the correct identification of tagged keywords master thesis search engine optimization the frequency of a certain word is below the threshold, the word is assigned to be a keyword.
If the word has not been seen before, it is automatically assigned as the keyword as it is considered rare enough as it has not appeared in the training corpus.
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