Abstract: A method, a structure, and a computer system for unidirectional text comparison. The exemplary embodiments may include determining a first similarity score between a first text string and a second text string, and computing an error term between the first text string and the second text string, wherein the error term incorporates a directionality of the first text string and the second text string. The exemplary embodiments may further include determining a second similarity score based on the first similarity score and the error term.

Abstract: Object similarity identification and ranking includes identifying multi-feature objects similar to a target object. The identifying is based on a user-specified similarity metric between the target object and each of the multi-feature objects having a value that exceeds a user-specified threshold. One or more weighted performance metrics is determined by weighting each of one or more performance metrics based on a customization input of a user. A gain score is generated for each of the multi-feature objects, each of the gain scores based on the one or more weighted performance metrics and a base score. A recommendation is output, the recommendation based on ranking the multi-feature objects according to the gain score of each multi-feature.

Abstract: An embodiment for identifying skill adjacencies and skill gaps to generate reskilling recommendations. The embodiment may receive input from a user including candidate details and a job description. The embodiment may automatically extract a first set of skill keywords from the candidate description and a second set of skill keywords from the job description. The embodiment may automatically input the first and second set of skill keywords into a first type of word embedding model and a second type of word embedding model to automatically generate word embeddings. The embodiment may automatically compare the generated word embeddings and calculate cosine similarity scores for the first and second set of skill keywords.

Abstract: A method, a structure, and a computer system for unidirectional text comparison. The exemplary embodiments may include determining a first similarity score between a first text string and a second text string, and computing an error term between the first text string and the second text string, wherein the error term incorporates a directionality of the first text string and the second text string. The exemplary embodiments may further include determining a second similarity score based on the first similarity score and the error term.

Abstract: Text blocks are semantically compared, and a semantic score is provided to a user. The semantic score is based on application of a machine learning model trained on a text corpus. One or both of the two text blocks may have one or more words that do not appear in the training text corpus (skip-words). Skip-words are used, rather than discarded, to adjust the semantic score via, for example, a penalization function. The user provides feedback about the accuracy of the adjusted semantic score, and the feedback is used to perform supervised learning model.

Abstract: A system identifies a first text fragment as being under evaluation, wherein the first text is in at least a first document being compared to a second document using a first semantic model. The system compares the first text fragment to one or more text fragments of the second document. The system identifies top-k text fragments in the second document that are most similar to the first text fragment based on processing using the first semantic model. The system presents a user, via a graphical user interface (GUI), the top-k text fragments in visual proximity to the first text fragment.

Abstract: Text blocks are semantically compared, and a semantic score is provided to a user. The semantic score is based on application of a machine learning model trained on a text corpus. One or both of the two text blocks may have one or more words that do not appear in the training text corpus (skip-words). Skip-words are used, rather than discarded, to adjust the semantic score via, for example, a penalization function. The user provides feedback about the accuracy of the adjusted semantic score, and the feedback is used to perform supervised learning model.

Abstract: A system identifies a first text fragment as being under evaluation, wherein the first text is in at least a first document being compared to a second document using a first semantic model. The system compares the first text fragment to one or more text fragments of the second document. The system identifies top-k text fragments in the second document that are most similar to the first text fragment based on processing using the first semantic model. The system presents a user, via a graphical user interface (GUI), the top-k text fragments in visual proximity to the first text fragment.

Abstract: An example method is provided in one example embodiment and may include maintaining a count of packets forwarded to a target evolved Node B (eNodeB) from a source eNodeB during a handover of a user equipment (UE) from the source eNodeB to the target eNodeB; and communicating an end marker indication message from the source eNodeB to the target eNodeB including the count of packets forwarded to the target eNodeB upon handover of the UE to the target eNodeB. A count of packets can be maintained for each bearer of the UE. A separate end marker indication message can be communicated to the target eNodeB for each bearer of the UE. The count of packets can be included in a Private Extension Information Element (IE) of the end marker indication message. In some embodiments, Radio Link Control (RLC) tuning parameters can be included in the Private Extension IE.

Abstract: An example method is provided in one example embodiment and may include maintaining a count of packets forwarded to a target evolved Node B (eNodeB) from a source eNodeB during a handover of a user equipment (UE) from the source eNodeB to the target eNodeB; and communicating an end marker indication message from the source eNodeB to the target eNodeB including the count of packets forwarded to the target eNodeB upon handover of the UE to the target eNodeB. A count of packets can be maintained for each bearer of the UE. A separate end marker indication message can be communicated to the target eNodeB for each bearer of the UE. The count of packets can be included in a Private Extension Information Element (IE) of the end marker indication message. In some embodiments, Radio Link Control (RLC) tuning parameters can be included in the Private Extension IE.

Abstract: An example method for facilitating automatic neighbor relation in a wireless telecommunication network environment is provided and includes initiating a fake handover with a target Evolved Node B (eNB) in a Long Term Evolution (LTE) wireless network environment over an S1 interface with a mobility management entity (MME), receiving a handover command message from the MME including neighbor cell information associated with the target eNB, canceling the fake handover, and updating a neighbor relations table (NRT) with the neighbor cell information. In various embodiments, the neighbor cell information includes information associated with the target eNB not communicated over an X2 interface, such as system control information communicated in a broadcast control channel. The method may further include receiving a measurement report from a user equipment with information of neighboring cells and initiating the fake handover if any neighboring cell included in the measurement report is missing from the NRT.

Abstract: An example method for facilitating automatic neighbor relation in a wireless telecommunication network environment is provided and includes initiating a fake handover with a target Evolved Node B (eNB) in a Long Term Evolution (LTE) wireless network environment over an S1 interface with a mobility management entity (MME), receiving a handover command message from the MME including neighbor cell information associated with the target eNB, canceling the fake handover, and updating a neighbor relations table (NRT) with the neighbor cell information. In various embodiments, the neighbor cell information includes information associated with the target eNB not communicated over an X2 interface, such as system control information communicated in a broadcast control channel. The method may further include receiving a measurement report from a user equipment with information of neighboring cells and initiating the fake handover if any neighboring cell included in the measurement report is missing from the NRT.