# Similarity Calculation via Passage-Level Event Connection Graph
## 2 Related Work  
The rapid advance of internet technology brings the explosive increase of web data. Facing the massive amount of data, internet users require automatic data analysis and processing tools. Text is one of the most prevailing data formats on the web. Thus, many web applications are designed aiming at processing textual data. Almost all the text related applications treat text similarity calculation as their fundamental module. Such as text clustering [3], machine dialogue [4], product recommendation [5], Q&A [6], those applications take text similarity calculation as the key component. In general, the methods for text similarity calculation can be partitioned into two categories. One is supervised based which is guided by annotated training samples. The other one is unsupervised based free from annotations. Supervised type often treats texts as points mapping to the high-dimensional space. A classification function is trained to separate points into similar and dissimilar two groups. Some other methods turn classification to a rank problem, which learn score functions to discriminate similar points from dissimilar ones. The advantage of supervised type is brought from the guidance of training data. Due to training process, supervised type often acquires high performance. Text is encoded as a vector for calculating convenience. Before the appearance of deep neuron network, one-hot vector is widely used. Only one entry has non-zero value. This kind of encoder generates high-dimensional and sparse vectors, which degrades the quality of many text-oriented applications [7]. The proposal of word embedding changes this dilemma. Word embedding compresses one-hot vector into a densely distributional vector with low dimension. Skip-gram [8], CBOW [9], GloVe [10], ELMo [11] are typical exemplars. The neuron-based models, such as CNN [12], GRU [13], LSTM [14], or the pre-trained language models such as Transformer [15], GPT [16], BERT [17], XLNET [18], Roberta [19] can produce more reasonable text representation on the basis of word embedding. The overlapping degree decides the similarity between texts, whereas, only depending on word concurrence or word alignment cannot fully express the semantic similarity between texts. To better model the interaction between texts, attention mechanism is taken, which considers the relevance of non-aligned parts across the input sequences. The widely applied attentions are multiple layer attention in [20] and co-attention in [21]. Basically, supervised text similarity calculations own high performance, especially after the application of neuron-based models. However, they are easily distorted by training data. They have to make a hypothesis about the distribution of input data in terms of the transcendental knowledge implicitly provided by training data. There is no way to collect enough training data to let supervised calculations go through in advance, especially for the neural-based methods, since their explosive parameters require massive data for fully training. For this reason, supervised calculations are appropriate to deal with domain data and can hardly be transferred. In our paper, we hope to design a text similarity calculation, which can fit to the texts in any type and from any domain. Therefore, we try to design an unsupervised text similarity calculation. Unsupervised similarity calculations free from training data. They model input data all by their natural distribution. Some untrained score functions are taken to measure text similarity based on distribution similarity. Euclidean distance [22], KL divergence [23], and Entropy [24] are some widely used score functions. Joint functions are also proposed to integrate previous scores [25; 26]. Due to missing training data, the features used by score functions are some statistical values provided by raw texts after word segmentation and stemming, such as TF/IDF [27], TextRank [28], and LDA [29]. Some recent works try to turn unsupervised similarity calculation into a supervised task.